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Pulak Ghosh

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Shashwat Alok & Pulak Ghosh & Nirupama Kulkarni & Manju Puri, 2024. "Breaking Barriers to Financial Access: Cross-Platform Digital Payments and Credit Markets," NBER Working Papers 33259, National Bureau of Economic Research, Inc.

    Cited by:

    1. Vives, Xavier & Ye, Zhiqiang, 2024. "Fintech Entry, Lending Market Competition, and Welfare," CEPR Discussion Papers 19245, C.E.P.R. Discussion Papers.
    2. Agur, Itai & Ari, Anil & Dell’Ariccia, Giovanni, 2025. "Bank competition and household privacy in a digital payment monopoly," Journal of Financial Economics, Elsevier, vol. 166(C).

  2. Marco Di Maggio & Pulak Ghosh & Soumya Kanti Ghosh & Andrew Wu, 2024. "Impact of Retail CBDC on Digital Payments, and Bank Deposits: Evidence from India," NBER Working Papers 32457, National Bureau of Economic Research, Inc.

    Cited by:

    1. Georgarakos, Dimitris & Kenny, Geoff & Laeven, Luc & Meyer, Justus, 2025. "Consumer attitudes towards a central bank digital currency," Working Paper Series 3035, European Central Bank.

  3. D'Acunto, Francesco & Ghosh, Pulak & Jain, Rajiv & Rossi, Alberto G., 2022. "How costly are cultural biases?," LawFin Working Paper Series 34, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).

    Cited by:

    1. Kaawach, Said & Kowalewski, Oskar & Talavera, Oleksandr, 2024. "Automatic versus manual investing: Role of past performance," Journal of Financial Stability, Elsevier, vol. 74(C).
    2. Said Kaawach & Oskar Kowalewski & Oleksandr Talavera, 2023. "Automatic vs Manual Investing: Role of Past Performance," Discussion Papers 23-04, Department of Economics, University of Birmingham.

  4. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "Household Finances and Social Interaction: Bayesian Analysis of Household Panel Data," IZA Discussion Papers 8301, Institute of Labor Economics (IZA).

    Cited by:

    1. Yann Algan & Pierre Cahuc, 2010. "Inherited Trust and Growth," SciencePo Working papers Main hal-03384693, HAL.
    2. Rey-Ares, Lucía & Fernández-López, Sara & Castro-González, Sandra & Rodeiro-Pazos, David, 2021. "Does self-control constitute a driver of millennials’ financial behaviors and attitudes?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    3. Jiaxi Zhou & Guoxiong Zhao & Liuyang Yao, 2025. "Peer Effects and Rural Households’ Online Shopping Behavior: Evidence from China," Agriculture, MDPI, vol. 15(14), pages 1-19, July.
    4. Mariya Hake & Philipp Poyntner, 2022. "Keeping Up With the Novaks? Income Distribution as a Determinant of Household Debt in CESEE," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S1), pages 224-260, April.
    5. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.
    6. Brown, Sarah & Ghosh, Pulak & Pareek, Bhuvanesh & Taylor, Karl, 2021. "The protective role of saving: Bayesian analysis of British panel data," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 57-72.
    7. Xin Li & Yunjie Wei & Shouyang Wang, 2025. "Household finance research: A systematic bibliometric analysis of evolution, trends, and emerging research directions," Review of Economics of the Household, Springer, vol. 23(2), pages 839-867, June.
    8. Shakeba Foster, 2023. "Income inequality and household debt: Examining the impact of relative income on formal and informal debt in South Africa," WIDER Working Paper Series wp-2023-37, World Institute for Development Economic Research (UNU-WIDER).

  5. Ko, Stanley I. M. & Chong, Terence T. L. & Ghosh, Pulak, 2014. "Dirichlet Process Hidden Markov Multiple Change-point Model," MPRA Paper 57871, University Library of Munich, Germany.

    Cited by:

    1. Máximo Camacho & María Dolores Gadea & Ana Gómez Loscos, 2019. "A new approach to dating the reference cycle," Working Papers 1914, Banco de España.
    2. Arnaud Dufays, 2015. "Evolutionary Sequential Monte Carlo Samplers for Change-point Models," Cahiers de recherche 1518, CIRPEE.
    3. Chiara Lattanzi & Manuele Leonelli, 2019. "A changepoint approach for the identification of financial extreme regimes," Papers 1902.09205, arXiv.org.

  6. Sarah Brown & Pulak Ghosh & Karl Taylor, 2012. "The Existence and Persistence of Household Financial Hardship," Working Papers 2012022, The University of Sheffield, Department of Economics.

    Cited by:

    1. Xu, Yilan & Briley, Daniel A. & Brown, Jeffrey R. & Roberts, Brent W., 2017. "Genetic and environmental influences on household financial distress," Journal of Economic Behavior & Organization, Elsevier, vol. 142(C), pages 404-424.
    2. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    3. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.
    4. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    5. Bartolucci, Francesco & Pigini, Claudia, 2017. "Granger causality in dynamic binary short panel data models," MPRA Paper 77486, University Library of Munich, Germany.

  7. Ausín Olivera, María Concepción & Galeano, Pedro & Ghosh, Pulak, 2010. "A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation," DES - Working Papers. Statistics and Econometrics. WS ws103822, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Xiaoning Kang & Xinwei Deng & Kam‐Wah Tsui & Mohsen Pourahmadi, 2020. "On variable ordination of modified Cholesky decomposition for estimating time‐varying covariance matrices," International Statistical Review, International Statistical Institute, vol. 88(3), pages 616-641, December.
    2. Mark J Jensen & John M Maheu, 2012. "Bayesian semiparametric multivariate GARCH modeling," Working Papers tecipa-458, University of Toronto, Department of Economics.
    3. Ellington, Michael & Kalli, Maria, 2025. "Predictive distributions and the market return: The role of market illiquidity," European Journal of Operational Research, Elsevier, vol. 323(1), pages 309-322.
    4. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2016. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 814-829.
    5. Manabu Asai & Michael McAleer, 2022. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
    6. Audronė Virbickaitė & Hedibert F. Lopes & M. Concepción Ausín & Pedro Galeano, 2019. "Particle learning for Bayesian semi-parametric stochastic volatility model," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1007-1023, October.
    7. Delatola, E.-I. & Griffin, J.E., 2013. "A Bayesian semiparametric model for volatility with a leverage effect," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 97-110.
    8. Yu, Jing-Rung & Chiou, Wan-Jiun Paul & Mu, Da-Ren, 2015. "A linearized value-at-risk model with transaction costs and short selling," European Journal of Operational Research, Elsevier, vol. 247(3), pages 872-878.
    9. Weixuan Zhu & Fabrizio Leisen, 2015. "A multivariate extension of a vector of two-parameter Poisson-Dirichlet processes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(1), pages 89-105, March.
    10. Xibin Zhang & Maxwell L. King, 2013. "Gaussian kernel GARCH models," Monash Econometrics and Business Statistics Working Papers 19/13, Monash University, Department of Econometrics and Business Statistics.
    11. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    12. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    13. Audrone Virbickaite & Hedibert F. Lopes, 2018. "Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model," DEA Working Papers 89, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    14. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    15. Lourme, Alexandre & Maurer, Frantz, 2017. "Testing the Gaussian and Student's t copulas in a risk management framework," Economic Modelling, Elsevier, vol. 67(C), pages 203-214.
    16. Fernández, Arturo J., 2015. "Optimum attributes component test plans for k-out-of-n:F Weibull systems using prior information," European Journal of Operational Research, Elsevier, vol. 240(3), pages 688-696.
    17. Martina Danielova Zaharieva & Mark Trede & Bernd Wilfling, 2017. "Bayesian semiparametric multivariate stochastic volatility with an application to international stock-market co-movements," CQE Working Papers 6217, Center for Quantitative Economics (CQE), University of Muenster.
    18. Huang, Yan & Kou, Gang & Peng, Yi, 2017. "Nonlinear manifold learning for early warnings in financial markets," European Journal of Operational Research, Elsevier, vol. 258(2), pages 692-702.
    19. Yong Shi & Wei Dai & Wen Long & Bo Li, 2021. "Deep Kernel Gaussian Process Based Financial Market Predictions," Papers 2105.12293, arXiv.org.

Articles

  1. Sumit Agarwal & Pulak Ghosh & Tianyue Ruan & Yunqi Zhang, 2024. "Transient Customer Response to Data Breaches of Their Information," Management Science, INFORMS, vol. 70(6), pages 4105-4114, June.

    Cited by:

    1. Huang, Zhehao & Dong, Hao & Liu, Zhaofei & Albitar, Khaldoon, 2025. "Unleashing the empowered effect of data resource on inclusive green growth: Based on double machine learning," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1270-1290.

  2. Agarwal, Sumit & Ghosh, Pulak & Zheng, Huanhuan, 2024. "Consumption response to a natural disaster: Evidence of price and income shocks from Chennai flood," Energy Economics, Elsevier, vol. 131(C).

    Cited by:

    1. Marco Guerzoni & Luigi Riso & Maria Grazia Zoia, 2025. "Forecasting the Impact of Extreme Weather Events on Electricity Prices in Italy: A GARCH-MIDAS Approach with Enhanced Variable Selection," DISCE - Working Papers del Dipartimento di Politica Economica dipe0043, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).

  3. Sumit Agarwal & Pulak Ghosh & Jing Li & Tianyue Ruan, 2024. "Digital Payments and Consumption: Evidence from the 2016 Demonetization in India," The Review of Financial Studies, Society for Financial Studies, vol. 37(8), pages 2550-2585.

    Cited by:

    1. Tang, Rong & Pu, Shi & Chen, Shou, 2024. "Present-biased preferences and the effect of illiquid assets," Economics Letters, Elsevier, vol. 244(C).
    2. Mayer, Pascal, 2025. "Going global, going digital: Firm internationalisation and digital resource use," Discussion Papers of the Institute for Organisational Economics 1/2025, University of Münster, Institute for Organisational Economics.

  4. Sarah Brown & Pulak Ghosh & Daniel Gray & Bhuvanesh Pareek & Jennifer Roberts, 2021. "Saving behaviour and health: A high-dimensional Bayesian analysis of British panel data," The European Journal of Finance, Taylor & Francis Journals, vol. 27(16), pages 1581-1603, November.

    Cited by:

    1. Davillas, Apostolos & Pudney, Stephen, 2017. "Concordance of health states in couples: Analysis of self-reported, nurse administered and blood-based biomarker data in the UK Understanding Society panel," Journal of Health Economics, Elsevier, vol. 56(C), pages 87-102.
    2. Benzeval, Michaela & Davillas, Apostolos & Kumari, Meena & Lynn, Peter, 2014. "Understanding Society: The UK Household Longitudinal Study Biomarker User Guide and Glossary," MPRA Paper 114713, University Library of Munich, Germany.

  5. Kiranmoy Das & Pulak Ghosh & Michael J. Daniels, 2021. "Modeling Multiple Time-Varying Related Groups: A Dynamic Hierarchical Bayesian Approach With an Application to the Health and Retirement Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 558-568, April.

    Cited by:

    1. Shubhajit Sen & Damitri Kundu & Kiranmoy Das, 2024. "A flexible Bayesian variable selection approach for modeling interval data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(1), pages 267-286, March.
    2. Kiranmoy Das & Bhuvanesh Pareek & Sarah Brown & Pulak Ghosh, 2022. "A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study," Computational Statistics, Springer, vol. 37(2), pages 837-863, April.
    3. Sweata Sen & Damitri Kundu & Kiranmoy Das, 2023. "Variable selection for categorical response: a comparative study," Computational Statistics, Springer, vol. 38(2), pages 809-826, June.
    4. Priya Kedia & Damitri Kundu & Kiranmoy Das, 2023. "A Bayesian variable selection approach to longitudinal quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 149-168, March.

  6. Jayabrata Biswas & Pulak Ghosh & Kiranmoy Das, 2020. "A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 261-283, June.

    Cited by:

    1. Kiranmoy Das & Bhuvanesh Pareek & Sarah Brown & Pulak Ghosh, 2022. "A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study," Computational Statistics, Springer, vol. 37(2), pages 837-863, April.
    2. Priya Kedia & Damitri Kundu & Kiranmoy Das, 2023. "A Bayesian variable selection approach to longitudinal quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 149-168, March.
    3. Danúbia R. Cunha & Jose Angelo Divino & Helton Saulo, 2024. "Zero-Adjusted Log-Symmetric Quantile Regression Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2087-2111, May.
    4. Jayabrata Biswas & Kiranmoy Das, 2021. "A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data," Computational Statistics, Springer, vol. 36(1), pages 241-260, March.

  7. Trambak Banerjee & Gourab Mukherjee & Shantanu Dutta & Pulak Ghosh, 2020. "A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 538-554, April.

    Cited by:

    1. Philipp Brüggemann & Nina Lehmann-Zschunke, 2023. "How to reduce termination on freemium platforms—literature review and empirical analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 707-721, December.
    2. Bikram Karmakar & Peng Liu & Gourab Mukherjee & Hai Che & Shantanu Dutta, 2022. "Improved retention analysis in freemium role‐playing games by jointly modelling players’ motivation, progression and churn," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 102-133, January.

  8. Bhuvanesh Pareek & Qiang Liu & Pulak Ghosh, 2019. "Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 197-223, January.

    Cited by:

    1. Qiang Liu & Hongju Liu & Manohar Kalwani, 2020. "“See your doctor”: the impact of direct-to-consumer advertising on patients with different affliction levels," Marketing Letters, Springer, vol. 31(1), pages 37-48, March.
    2. Cedric Thomas Silveira, 2023. "Decoding a Doctor’s Prescription: A Study," Jindal Journal of Business Research, , vol. 12(1), pages 73-84, June.

  9. Voleti, Sudhir & Srinivasan, V. & Ghosh, Pulak, 2017. "An approach to improve the predictive power of choice-based conjoint analysis," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 325-335.

    Cited by:

    1. Weber, Anett & Steiner, Winfried J., 2021. "Modeling price response from retail sales: An empirical comparison of models with different representations of heterogeneity," European Journal of Operational Research, Elsevier, vol. 294(3), pages 843-859.
    2. Verena Sablotny-Wackershauser & Marcel Lichters & Daniel Guhl & Paul Bengart & Bodo Vogt, 2024. "Crossing incentive alignment and adaptive designs in choice-based conjoint: A fruitful endeavor," Journal of the Academy of Marketing Science, Springer, vol. 52(3), pages 610-633, May.
    3. Nils Goeken & Peter Kurz & Winfried J. Steiner, 2024. "Multimodal preference heterogeneity in choice-based conjoint analysis: a simulation study," Journal of Business Economics, Springer, vol. 94(1), pages 137-185, January.
    4. Hein, Maren & Goeken, Nils & Kurz, Peter & Steiner, Winfried J., 2022. "Using Hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: A study based on conjoint choice data," European Journal of Operational Research, Elsevier, vol. 297(2), pages 630-651.
    5. Barwitz, Niklas, 2020. "The relevance of interaction choice: Customer preferences and willingness to pay," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    6. Narine Yegoryan & Daniel Guhl & Friederike Paetz, 2023. "When Zeros Count: Confounding in Preference Heterogeneity and Attribute Non-attendance," Rationality and Competition Discussion Paper Series 482, CRC TRR 190 Rationality and Competition.
    7. Kosmo Karantonis & Oliver Schnittka & Mario Farsky & Henrik Sattler, 2025. "Empirical validation of a new technique (‘select-and-rank’) to measure brand preference," Journal of Brand Management, Palgrave Macmillan, vol. 32(3), pages 227-237, May.

  10. Karthik Sriram & R. V. Ramamoorthi & Pulak Ghosh, 2016. "On Bayesian Quantile Regression Using a Pseudo-joint Asymmetric Laplace Likelihood," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(1), pages 87-104, February.

    Cited by:

    1. Tian, Yuzhu & Zhu, Qianqian & Tian, Maozai, 2016. "Estimation of linear composite quantile regression using EM algorithm," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 183-191.
    2. Yingying Hu & Huixia Judy Wang & Xuming He & Jianhua Guo, 2021. "Bayesian joint-quantile regression," Computational Statistics, Springer, vol. 36(3), pages 2033-2053, September.
    3. Adam, Timo & Mayr, Andreas & Kneib, Thomas, 2022. "Gradient boosting in Markov-switching generalized additive models for location, scale, and shape," Econometrics and Statistics, Elsevier, vol. 22(C), pages 3-16.

  11. Karthik Sriram & Peng Shi & Pulak Ghosh, 2016. "A Bayesian quantile regression model for insurance company costs data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 177-202, January.

    Cited by:

    1. Eling, Martin & Jia, Ruo & Schaper, Philipp, 2017. "Get the Balance Right: A Simultaneous Equation Model to Analyze Growth, Profitability, and Safety," Working Papers on Finance 1716, University of St. Gallen, School of Finance.
    2. PERUŠKO Ticijan, 2020. "Accounting Information for Improvement of Cost Planning in Accident Insurance," Journal of Economic and Social Development, Clinical Journals Press, vol. 7(01), pages 01-10.
    3. Zijian Zeng & Meng Li, 2020. "Bayesian Median Autoregression for Robust Time Series Forecasting," Papers 2001.01116, arXiv.org, revised Dec 2020.
    4. Yonggang Ji & Haifang Shi, 2020. "Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-34, October.
    5. Zeng, Zijian & Li, Meng, 2021. "Bayesian median autoregression for robust time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1000-1010.

  12. Sarah Brown & Pulak Ghosh & Karl Taylor, 2016. "Household Finances and Social Interaction: Bayesian Analysis of Household Panel Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(3), pages 467-488, September.
    See citations under working paper version above.
  13. Arnab Mukherji & Satrajit Roychoudhury & Pulak Ghosh & Sarah Brown, 2016. "Estimating Health Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1140-1158, September.

    Cited by:

    1. Minke Remmerswaal & Jan Boone, 2020. "A Structural Microsimulation Model for Demand-Side Cost-Sharing in Healthcare," CPB Discussion Paper 415, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Boone, Jan & Remmerswaal, Minke, 2024. "A structural microsimulation model for demand-side cost-sharing in healthcare," Journal of Health Economics, Elsevier, vol. 97(C).
    3. Kiranmoy Das & Bhuvanesh Pareek & Sarah Brown & Pulak Ghosh, 2022. "A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study," Computational Statistics, Springer, vol. 37(2), pages 837-863, April.
    4. Jayabrata Biswas & Pulak Ghosh & Kiranmoy Das, 2020. "A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 261-283, June.
    5. Priya Kedia & Damitri Kundu & Kiranmoy Das, 2023. "A Bayesian variable selection approach to longitudinal quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 149-168, March.
    6. Jayabrata Biswas & Kiranmoy Das, 2021. "A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data," Computational Statistics, Springer, vol. 36(1), pages 241-260, March.
    7. Anne Mason & Idaira Rodriguez Santana & María José Aragón & Nigel Rice & Martin Chalkley & Raphael Wittenberg & Jose-Luis Fernandez, 2019. "Drivers of health care expenditure: Final report," Working Papers 169cherp, Centre for Health Economics, University of York.

  14. Brown, Sarah & Ghosh, Pulak & Su, Li & Taylor, Karl, 2015. "Modelling household finances: A Bayesian approach to a multivariate two-part model," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 190-207.

    Cited by:

    1. Feng, Xiangnan & Lu, Bin & Song, Xinyuan & Ma, Shuang, 2019. "Financial literacy and household finances: A Bayesian two-part latent variable modeling approach," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 119-137.
    2. Amel Attour & Marco Baudino & Jackie Krafft & Nathalie Lazaric, 2020. "Determinants of smart energy tracking application use at the city level: Evidence from France," Post-Print hal-02942483, HAL.
    3. Jayabrata Biswas & Pulak Ghosh & Kiranmoy Das, 2020. "A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 261-283, June.
    4. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.
    5. Dittmann Iwona, 2016. "Rates of Return on Open-End Debt Investment Funds and Bank Deposits in Poland in the Years 1995–2015 – A Comparative Analysis," Folia Oeconomica Stetinensia, Sciendo, vol. 16(1), pages 93-112, December.
    6. Alessandro Bucciol & Raffaele Miniaci & Sergio Pastorello, 2015. "Return Expectations and Risk Aversion Heterogeneity in Household Portfolios," Working Papers 01/2015, University of Verona, Department of Economics.
    7. Brown, Sarah & Ghosh, Pulak & Pareek, Bhuvanesh & Taylor, Karl, 2021. "The protective role of saving: Bayesian analysis of British panel data," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 57-72.
    8. Attour, Amel & Baudino, Marco & Krafft, Jackie & Lazaric, Nathalie, 2020. "Determinants of energy tracking application use at the city level: Evidence from France," Energy Policy, Elsevier, vol. 147(C).
    9. Jayabrata Biswas & Kiranmoy Das, 2021. "A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data," Computational Statistics, Springer, vol. 36(1), pages 241-260, March.

  15. Janakiraman Moorthy & Rangin Lahiri & Neelanjan Biswas & Dipyaman Sanyal & Jayanthi Ranjan & Krishnadas Nanath & Pulak Ghosh, 2015. "Big Data: Prospects and Challenges," Vikalpa: The Journal for Decision Makers, , vol. 40(1), pages 74-96, March.

    Cited by:

    1. LaBrie, Ryan C. & Steinke, Gerhard H. & Li, Xiangmin & Cazier, Joseph A., 2018. "Big data analytics sentiment: US-China reaction to data collection by business and government," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 45-55.
    2. Nani, Albi, 2023. "Valuing big data: An analysis of current regulations and proposal of frameworks," International Journal of Accounting Information Systems, Elsevier, vol. 51(C).
    3. Rebecca Dingus & Hulda G. Black & Nicole A. Flink, 2024. "Analytics for all marketing majors: sparking interest in the uninterested," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 126-141, June.
    4. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    5. Cong Cheng & Mengxin Zhang, 2023. "Conceptualizing Corporate Digital Responsibility: A Digital Technology Development Perspective," Sustainability, MDPI, vol. 15(3), pages 1-21, January.

  16. Sudhir Voleti & Praveen K. Kopalle & Pulak Ghosh, 2015. "An Interproduct Competition Model Incorporating Branding Hierarchy and Product Similarities Using Store-Level Data," Management Science, INFORMS, vol. 61(11), pages 2720-2738, November.

    Cited by:

    1. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    2. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    3. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    5. Amit Mehra & Sajeesh Sajeesh & Sudhir Voleti, 2020. "Impact of Reference Prices on Product Positioning and Profits," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 882-892, April.
    6. Zhiyi Wang & Lusi Yang & Jungpil Hahn, 2023. "Winner Takes All? The Blockbuster Effect on Crowdfunding Platforms," Information Systems Research, INFORMS, vol. 34(3), pages 935-960, September.

  17. Stöber, Jakob & Hong, Hyokyoung Grace & Czado, Claudia & Ghosh, Pulak, 2015. "Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 28-39.

    Cited by:

    1. Roger M. Cooke & Harry Joe & Bo Chang, 2020. "Vine copula regression for observational studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 141-167, June.
    2. Genest Christian & Scherer Matthias, 2019. "The world of vines: An interview with Claudia Czado," Dependence Modeling, De Gruyter, vol. 7(1), pages 169-180, January.
    3. Hobæk Haff, Ingrid & Aas, Kjersti & Frigessi, Arnoldo & Lacal, Virginia, 2016. "Structure learning in Bayesian Networks using regular vines," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 186-208.
    4. Chu, Amanda M.Y. & Ip, Chun Yin & Lam, Benson S.Y. & So, Mike K.P., 2022. "Vine copula statistical disclosure control for mixed-type data," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    5. Pan Shenyi & Joe Harry, 2024. "Assessing copula models for mixed continuous-ordinal variables," Dependence Modeling, De Gruyter, vol. 12(1), pages 1-18.
    6. Zilko, Aurelius A. & Kurowicka, Dorota, 2016. "Copula in a multivariate mixed discrete–continuous model," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 28-55.
    7. Yang, Cheng & Yin, Weihao & Liu, Xueting & Huang, Yanwen & Lu, Dagang & Zhang, Jie, 2024. "Tornado-induced risk analysis of railway system considering the correlation of parameters," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    8. Shi, Peng & Zhao, Zifeng, 2024. "Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction," Journal of Econometrics, Elsevier, vol. 240(1).
    9. Panagiotelis, Anastasios & Czado, Claudia & Joe, Harry & Stöber, Jakob, 2017. "Model selection for discrete regular vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 138-152.
    10. Chang, Bo & Joe, Harry, 2019. "Prediction based on conditional distributions of vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 45-63.
    11. Saeide Sefidi & Mojtaba Ganjali & Taban Baghfalaki, 2022. "Analysis of ordinal and continuous longitudinal responses using pair copula construction," METRON, Springer;Sapienza Università di Roma, vol. 80(2), pages 255-280, August.
    12. Brida Juan Gabriel & Moreno Leonardo & Scaglione Miriam, 2024. "Modeling multivariate tourism expenditure using vine copula: empirical findings from of Fribourg-Switzerland," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4093-4116, October.
    13. Aas Kjersti & Nagler Thomas & Jullum Martin & Løland Anders, 2021. "Explaining predictive models using Shapley values and non-parametric vine copulas," Dependence Modeling, De Gruyter, vol. 9(1), pages 62-81, January.

  18. Ausín, M. Concepción & Galeano, Pedro & Ghosh, Pulak, 2014. "A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation," European Journal of Operational Research, Elsevier, vol. 232(2), pages 350-358.
    See citations under working paper version above.
  19. Brown, Sarah & Ghosh, Pulak & Taylor, Karl, 2014. "The existence and persistence of household financial hardship: A Bayesian multivariate dynamic logit framework," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 285-298.

    Cited by:

    1. French, Declan, 2023. "Exploring household financial strain dynamics," International Review of Financial Analysis, Elsevier, vol. 86(C).
    2. Kirsch, Steffen & Burghof, Hans-Peter, 2018. "The efficiency of savings-linked relationship lending for housing finance," Journal of Housing Economics, Elsevier, vol. 42(C), pages 55-68.
    3. Pigini, Claudia & Presbitero, Andrea F. & Zazzaro, Alberto, 2016. "State dependence in access to credit," Journal of Financial Stability, Elsevier, vol. 27(C), pages 17-34.
    4. French, Declan & Vigne, Samuel, 2019. "The causes and consequences of household financial strain: A systematic review," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 150-156.
    5. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    6. Liaqat Ali & Muhammad Kamran Naqi Khan & Habib Ahmad, 2020. "Financial Fragility of Pakistani Household," Journal of Family and Economic Issues, Springer, vol. 41(3), pages 572-590, September.
    7. Rasanga, Fiona & Harrison, Tina & Calabrese, Raffaella, 2024. "Measuring the energy poverty premium in Great Britain and identifying its main drivers based on longitudinal household survey data," Energy Economics, Elsevier, vol. 136(C).
    8. Lei, Lei & Lu, Weijie & Niu, Geng & Zhou, Yang, 2024. "Religiosity and financial distress of the young," Journal of Banking & Finance, Elsevier, vol. 168(C).
    9. Chichaibelu, Bezawit Beyene & Waibel, Hermann, 2018. "Over-indebtedness and its persistence in rural households in Thailand and Vietnam," Journal of Asian Economics, Elsevier, vol. 56(C), pages 1-23.
    10. Bartolucci, Francesco & Pigini, Claudia, 2017. "Granger causality in dynamic binary short panel data models," MPRA Paper 77486, University Library of Munich, Germany.
    11. Xin Li & Yunjie Wei & Shouyang Wang, 2025. "Household finance research: A systematic bibliometric analysis of evolution, trends, and emerging research directions," Review of Economics of the Household, Springer, vol. 23(2), pages 839-867, June.

  20. Man-Wai Ho & Wanzhu Tu & Pulak Ghosh & Ram C. Tiwari, 2013. "A Nested Dirichlet Process Analysis of Cluster Randomized Trial Data With Application in Geriatric Care Assessment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 48-68, March.

    Cited by:

    1. Sudhir Voleti & Praveen K. Kopalle & Pulak Ghosh, 2015. "An Interproduct Competition Model Incorporating Branding Hierarchy and Product Similarities Using Store-Level Data," Management Science, INFORMS, vol. 61(11), pages 2720-2738, November.

  21. Brian Neelon & Pulak Ghosh & Patrick F. Loebs, 2013. "A spatial Poisson hurdle model for exploring geographic variation in emergency department visits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 389-413, February.

    Cited by:

    1. Cindy Xin Feng, 2021. "A comparison of zero-inflated and hurdle models for modeling zero-inflated count data," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-19, December.
    2. Zhen Yu & Keming Yu & Wolfgang K. Härdle & Xueliang Zhang & Kai Wang & Maozai Tian, 2022. "Bayesian spatio‐temporal modeling for the inpatient hospital costs of alcohol‐related disorders," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 644-667, December.
    3. Eugenia Buta & Stephanie S. O’Malley & Ralitza Gueorguieva, 2018. "Bayesian joint modelling of longitudinal data on abstinence, frequency and intensity of drinking in alcoholism trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 869-888, June.
    4. Ali Arab, 2015. "Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros," IJERPH, MDPI, vol. 12(9), pages 1-13, August.
    5. Lawrence N Kazembe, 2013. "A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-10, September.
    6. Zaida C. Quiroz & Marcos O. Prates & Håvard Rue, 2015. "A Bayesian approach to estimate the biomass of anchovies off the coast of Perú," Biometrics, The International Biometric Society, vol. 71(1), pages 208-217, March.
    7. Soutik Ghosal & Timothy S. Lau & Jeremy Gaskins & Maiying Kong, 2020. "A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1121-1144, November.
    8. Derek S. Young & Andrew M. Raim & Nancy R. Johnson, 2017. "Zero-inflated modelling for characterizing coverage errors of extracts from the US Census Bureau's Master Address File," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 73-97, January.

  22. Sudhir Voleti & Pulak Ghosh, 2013. "A robust approach to measure latent, time-varying equity in hierarchical branding structures," Quantitative Marketing and Economics (QME), Springer, vol. 11(3), pages 289-319, September.

    Cited by:

    1. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    2. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    3. Amit Mehra & Sajeesh Sajeesh & Sudhir Voleti, 2020. "Impact of Reference Prices on Product Positioning and Profits," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 882-892, April.

  23. Zhang, Hongmei & Ghosh, Kaushik & Ghosh, Pulak, 2012. "Sampling designs via a multivariate hypergeometric-Dirichlet process model for a multi-species assemblage with unknown heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2562-2573.

    Cited by:

    1. Han, Shengtong & Zhang, Hongmei & Karmaus, Wilfried & Roberts, Graham & Arshad, Hasan, 2017. "Adjusting background noise in cluster analyses of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 93-104.

  24. Wanzhu Tu & Pulak Ghosh & Barry P. Katz, 2011. "A stochastic model for assessing Chlamydia trachomatis transmission risk by using longitudinal observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 975-989, October.

    Cited by:

    1. Gamage, Prabhashi W. Withana & McMahan, Christopher S. & Wang, Lianming & Tu, Wanzhu, 2018. "A Gamma-frailty proportional hazards model for bivariate interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 354-366.

  25. Young‐Hoon Park & Chang Hee Park & Pulak Ghosh, 2011. "Modelling member behaviour in on‐line user‐generated content sites: a semiparametric Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 1051-1069, October.

    Cited by:

    1. Voleti, Sudhir & Srinivasan, V. & Ghosh, Pulak, 2017. "An approach to improve the predictive power of choice-based conjoint analysis," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 325-335.
    2. Matteo Iacopini & Carlo Romano Marcello Alessandro Santagiustina, 2021. "Filtering the Intensity of Public Concern from Social Media Count Data with Jumps," SciencePo Working papers Main hal-04494229, HAL.

  26. Binbing Yu & Pulak Ghosh, 2010. "Joint Modeling for Cognitive Trajectory and Risk of Dementia in the Presence of Death," Biometrics, The International Biometric Society, vol. 66(1), pages 294-300, March.

    Cited by:

    1. Graeme L. Hickey & Pete Philipson & Andrea Jorgensen & Ruwanthi Kolamunnage‐Dona, 2018. "A comparison of joint models for longitudinal and competing risks data, with application to an epilepsy drug randomized controlled trial," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1105-1123, October.

  27. Basso, Rodrigo M. & Lachos, Víctor H. & Cabral, Celso Rômulo Barbosa & Ghosh, Pulak, 2010. "Robust mixture modeling based on scale mixtures of skew-normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2926-2941, December.

    Cited by:

    1. Camila Borelli Zeller & Celso Rômulo Barbosa Cabral & Víctor Hugo Lachos & Luis Benites, 2019. "Finite mixture of regression models for censored data based on scale mixtures of normal distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 89-116, March.
    2. Azzalini, Adelchi, 2022. "An overview on the progeny of the skew-normal family— A personal perspective," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    3. Sharon Lee & Geoffrey McLachlan, 2013. "Model-based clustering and classification with non-normal mixture distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 427-454, November.
    4. Abbas Mahdavi & Vahid Amirzadeh & Ahad Jamalizadeh & Tsung-I Lin, 2021. "Maximum likelihood estimation for scale-shape mixtures of flexible generalized skew normal distributions via selection representation," Computational Statistics, Springer, vol. 36(3), pages 2201-2230, September.
    5. Chun Yu & Weixin Yao & Guangren Yang, 2020. "A Selective Overview and Comparison of Robust Mixture Regression Estimators," International Statistical Review, International Statistical Institute, vol. 88(1), pages 176-202, April.
    6. Saverio Ranciati & Giuliano Galimberti & Gabriele Soffritti, 2019. "Bayesian variable selection in linear regression models with non-normal errors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 323-358, June.
    7. Víctor H. Lachos & Celso R. B. Cabral & Marcos O. Prates & Dipak K. Dey, 2019. "Flexible regression modeling for censored data based on mixtures of student-t distributions," Computational Statistics, Springer, vol. 34(1), pages 123-152, March.
    8. Cabral, Celso Rômulo Barbosa & Lachos, Víctor Hugo & Prates, Marcos O., 2012. "Multivariate mixture modeling using skew-normal independent distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 126-142, January.
    9. Tarpey, Thaddeus & Loperfido, Nicola, 2015. "Self-consistency and a generalized principal subspace theorem," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 27-37.
    10. Lachos, Víctor H. & Moreno, Edgar J. López & Chen, Kun & Cabral, Celso Rômulo Barbosa, 2017. "Finite mixture modeling of censored data using the multivariate Student-t distribution," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 151-167.
    11. Raúl Alejandro Morán-Vásquez & Edwin Zarrazola & Daya K. Nagar, 2023. "Some Theoretical and Computational Aspects of the Truncated Multivariate Skew-Normal/Independent Distributions," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    12. Abbas Mahdavi & Javier E. Contreras-Reyes, 2025. "Bounded data modeling using logit-skew-normal mixtures," Statistical Papers, Springer, vol. 66(3), pages 1-21, April.
    13. Ruijie Guan & Yaohua Rong & Weihu Cheng & Zhenyu Xin, 2025. "A Novel Finite Mixture Model Based on the Generalized t Distributions with Two-Sided Censored Data," Annals of Data Science, Springer, vol. 12(1), pages 341-379, February.
    14. Sugasawa, Shonosuke & Kobayashi, Genya, 2022. "Robust fitting of mixture models using weighted complete estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    15. Yana Melnykov & Xuwen Zhu & Volodymyr Melnykov, 2021. "Transformation mixture modeling for skewed data groups with heavy tails and scatter," Computational Statistics, Springer, vol. 36(1), pages 61-78, March.
    16. Morris, Katherine & Punzo, Antonio & McNicholas, Paul D. & Browne, Ryan P., 2019. "Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 145-166.
    17. Bart Keijsers & Bart Diris & Erik Kole, 2015. "Cyclicality in Losses on Bank Loans," Tinbergen Institute Discussion Papers 15-050/III, Tinbergen Institute, revised 01 Sep 2017.
    18. Francisco H. C. Alencar & Christian E. Galarza & Larissa A. Matos & Victor H. Lachos, 2022. "Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 521-557, September.
    19. Amandine Schmutz & Julien Jacques & Charles Bouveyron & Laurence Chèze & Pauline Martin, 2020. "Clustering multivariate functional data in group-specific functional subspaces," Computational Statistics, Springer, vol. 35(3), pages 1101-1131, September.
    20. Yang Yang & Lichun Wang, 2024. "A non-iteration Bayesian sampling algorithm for robust seemingly unrelated regression models $$^*$$ ∗," Computational Statistics, Springer, vol. 39(3), pages 1281-1300, May.
    21. McLachlan, Geoff & Lee, Sharon X, 2013. "EMMIXuskew: An R Package for Fitting Mixtures of Multivariate Skew t Distributions via the EM Algorithm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i12).
    22. Tiago Dias-Domingues & Helena Mouriño & Nuno Sepúlveda, 2024. "Classification Methods for the Serological Status Based on Mixtures of Skew-Normal and Skew-t Distributions," Mathematics, MDPI, vol. 12(2), pages 1-25, January.
    23. Mahdi Teimouri & Saralees Nadarajah, 2022. "Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 665-692, August.
    24. Libin Jin & Sung Nok Chiu & Jianhua Zhao & Lixing Zhu, 2023. "A constrained maximum likelihood estimation for skew normal mixtures," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(4), pages 391-419, May.
    25. Mohsen Maleki & Darren Wraith, 2019. "Mixtures of multivariate restricted skew-normal factor analyzer models in a Bayesian framework," Computational Statistics, Springer, vol. 34(3), pages 1039-1053, September.
    26. Prates, Marcos Oliveira & Lachos, Victor Hugo & Barbosa Cabral, Celso Rômulo, 2013. "mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i12).
    27. Wan-Lun Wang & Yu-Chen Yang & Tsung-I Lin, 2024. "Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 271-307, June.
    28. Jiwon Park & Dipak K. Dey & Víctor H. Lachos, 2024. "Finite mixture of regression models for censored data based on the skew-t distribution," Computational Statistics, Springer, vol. 39(7), pages 3695-3726, December.
    29. Melnykov, Volodymyr & Shen, Gang, 2013. "Clustering through empirical likelihood ratio," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 1-10.
    30. Chunzheng Cao & Mengqian Chen & Yahui Wang & Jian Qing Shi, 2018. "Heteroscedastic replicated measurement error models under asymmetric heavy-tailed distributions," Computational Statistics, Springer, vol. 33(1), pages 319-338, March.
    31. Fatma Zehra Doğru & Olcay Arslan, 2021. "Finite mixtures of skew Laplace normal distributions with random skewness," Computational Statistics, Springer, vol. 36(1), pages 423-447, March.
    32. Francisco H. C. Alencar & Larissa A Matos & Víctor H. Lachos, 2022. "Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 463-486, November.
    33. Wraith, Darren & Forbes, Florence, 2015. "Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 61-73.
    34. Ruijie Guan & Junjun Jiao & Weihu Cheng & Guozhi Hu, 2025. "A novel finite mixture model based on the generalized scale mixtures of asymmetric generalized normal distributions: properties, estimation methodology and applications," Computational Statistics, Springer, vol. 40(5), pages 2425-2470, June.
    35. Sharon Lee & Geoffrey McLachlan, 2013. "On mixtures of skew normal and skew $$t$$ -distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 241-266, September.
    36. Chunzheng Cao & Yahui Wang & Jian Qing Shi & Jinguan Lin, 2018. "Measurement Error Models for Replicated Data Under Asymmetric Heavy-Tailed Distributions," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 531-553, August.
    37. Lachos, Victor H. & Bandyopadhyay, Dipankar & Garay, Aldo M., 2011. "Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1208-1217, August.
    38. Mirfarah, Elham & Naderi, Mehrdad & Chen, Ding-Geng, 2021. "Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

  28. Ghosh, Pulak & Tu, Wanzhu, 2009. "Assessing Sexual Attitudes and Behaviors of Young Women: A Joint Model with Nonlinear Time Effects, Time Varying Covariates, and Dropouts," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 474-485.

    Cited by:

    1. Gamage, Prabhashi W. Withana & McMahan, Christopher S. & Wang, Lianming & Tu, Wanzhu, 2018. "A Gamma-frailty proportional hazards model for bivariate interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 354-366.
    2. De la Cruz, Rolando & Meza, Cristian & Arribas-Gil, Ana & Carroll, Raymond J., 2016. "Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 94-106.

  29. Ghosh, Pulak & Albert, Paul S., 2009. "A Bayesian analysis for longitudinal semicontinuous data with an application to an acupuncture clinical trial," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 699-706, January.

    Cited by:

    1. Shi, Peng & Feng, Xiaoping & Ivantsova, Anastasia, 2015. "Dependent frequency–severity modeling of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 417-428.
    2. Brian Neelon & A. James O'Malley & Sharon-Lise T. Normand, 2011. "A Bayesian Two-Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity," Biometrics, The International Biometric Society, vol. 67(1), pages 280-289, March.
    3. Yang, Yan & Simpson, Douglas, 2010. "Unified computational methods for regression analysis of zero-inflated and bound-inflated data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1525-1534, June.

  30. Ghosh, Pulak & Basu, Sanjib & Tiwari, Ram C., 2009. "Bayesian Analysis of Cancer Rates From SEER Program Using Parametric and Semiparametric Joinpoint Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 439-452.

    Cited by:

    1. Sudhir Voleti & Pulak Ghosh, 2013. "A robust approach to measure latent, time-varying equity in hierarchical branding structures," Quantitative Marketing and Economics (QME), Springer, vol. 11(3), pages 289-319, September.
    2. Voleti, Sudhir & Srinivasan, V. & Ghosh, Pulak, 2017. "An approach to improve the predictive power of choice-based conjoint analysis," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 325-335.
    3. Ausín Olivera, María Concepción & Galeano, Pedro & Ghosh, Pulak, 2010. "A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation," DES - Working Papers. Statistics and Econometrics. WS ws103822, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Erengul Dodd & Jonathan J. Forster & Jakub Bijak & Peter W. F. Smith, 2018. "Smoothing mortality data: the English Life Tables, 2010–2012," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 717-735, June.
    5. Ram C. Kafle & Netra Khanal & Chris P. Tsokos, 2014. "Bayesian age-stratified joinpoint regression model: an application to lung and brain cancer mortality," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2727-2742, December.
    6. Ghosh, Pulak & Huang, Lan & Yu, Binbing & Tiwari, Ram C., 2009. "Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4073-4082, October.

  31. Ghosh, Pulak & Bayes, C.L. & Lachos, V.H., 2009. "A robust Bayesian approach to null intercept measurement error model with application to dental data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1066-1079, February.

    Cited by:

    1. Azzalini, Adelchi, 2022. "An overview on the progeny of the skew-normal family— A personal perspective," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    2. Rendao Ye & Bingni Fang & Weixiao Du & Kun Luo & Yiting Lu, 2022. "Bootstrap Tests for the Location Parameter under the Skew-Normal Population with Unknown Scale Parameter and Skewness Parameter," Mathematics, MDPI, vol. 10(6), pages 1-23, March.

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