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Dealing with label switching in mixture models

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Cited by:

  1. Terrance Savitsky & Daniel McCaffrey, 2014. "Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 275-302, April.
  2. Antonio Punzo & Paul. D. McNicholas, 2017. "Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 249-293, July.
  3. Riccardo Rastelli & Michael Fop, 2020. "A stochastic block model for interaction lengths," 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. 14(2), pages 485-512, June.
  4. Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.
  5. Stefano Tonellato, 2017. "From Dirichlet Process mixture models to spectral clustering," Working Papers 2017:33, Department of Economics, University of Venice "Ca' Foscari".
  6. 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.
  7. Montanari, Giorgio E. & Doretti, Marco & Bartolucci, Francesco, 2017. "A multilevel latent Markov model for the evaluation of nursing homes' performance," MPRA Paper 80691, University Library of Munich, Germany.
  8. Bolano, Danilo & Berchtold, André, 2016. "General framework and model building in the class of Hidden Mixture Transition Distribution models," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 131-145.
  9. Christian Aßmann, 2015. "Rossi, Peter E.: Bayesian non- and semi-parametric methods and applications," Journal of Economics, Springer, vol. 115(2), pages 195-197, June.
  10. J. Griffin & M. Steel, 2008. "Flexible mixture modelling of stochastic frontiers," Journal of Productivity Analysis, Springer, vol. 29(1), pages 33-50, February.
  11. Rodríguez, Carlos E. & Núñez-Antonio, Gabriel & Escarela, Gabriel, 2020. "A Bayesian mixture model for clustering circular data," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  12. Grazian, Clara & Robert, Christian P., 2018. "Jeffreys priors for mixture estimation: Properties and alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 149-163.
  13. Kensuke Okada & Shin-ichi Mayekawa, 2018. "Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling," Computational Statistics, Springer, vol. 33(3), pages 1457-1473, September.
  14. Jesse D. Raffa & Joel A. Dubin, 2015. "Multivariate longitudinal data analysis with mixed effects hidden Markov models," Biometrics, The International Biometric Society, vol. 71(3), pages 821-831, September.
  15. Lian, Heng, 2010. "Sparse Bayesian hierarchical modeling of high-dimensional clustering problems," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1728-1737, August.
  16. Sijia Xiang & Weixin Yao, 2020. "Semiparametric mixtures of regressions with single-index for model based clustering," 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. 14(2), pages 261-292, June.
  17. Angelo Mazza & Antonio Punzo, 2020. "Mixtures of multivariate contaminated normal regression models," Statistical Papers, Springer, vol. 61(2), pages 787-822, April.
  18. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2016. "Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem," Journal of Econometrics, Elsevier, vol. 192(1), pages 190-206.
  19. E. Lázaro & C. Armero & V. Gómez-Rubio, 2020. "Approximate Bayesian inference for mixture cure models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 750-767, September.
  20. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
  21. Hu, Hao & Yao, Weixin & Wu, Yichao, 2017. "The robust EM-type algorithms for log-concave mixtures of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 14-26.
  22. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2014. "Bayesian analysis of dynamic factor models: An ex-post approach towards the rotation problem," Kiel Working Papers 1902, Kiel Institute for the World Economy (IfW).
  23. Alessio Farcomeni & Antonio Punzo, 2020. "Robust model-based clustering with mild and gross outliers," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 989-1007, December.
  24. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
  25. Arman Oganisian & Nandita Mitra & Jason A. Roy, 2021. "A Bayesian nonparametric model for zero‐inflated outcomes: Prediction, clustering, and causal estimation," Biometrics, The International Biometric Society, vol. 77(1), pages 125-135, March.
  26. James D. Hamilton & Daniel F. Waggoner & Tao Zha, 2007. "Normalization in Econometrics," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 221-252.
  27. Shotwell Matthew S & Slate Elizabeth H, 2010. "Bayesian Modeling of Footrace Finishing Times," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-21, July.
  28. Lubrano, Michel & Ndoye, Abdoul Aziz Junior, 2016. "Income inequality decomposition using a finite mixture of log-normal distributions: A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 830-846.
  29. 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.
  30. José Dias & Jeroen Vermunt, 2008. "A bootstrap-based aggregate classifier for model-based clustering," Computational Statistics, Springer, vol. 23(4), pages 643-659, October.
  31. Riccardo Rastelli & Michael Fop, 0. "A stochastic block model for interaction lengths," 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. 0, pages 1-28.
  32. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2016. "Non-parametric estimation of finite mixtures from repeated measurements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 211-229, January.
  33. Roy Costilla & Ivy Liu & Richard Arnold & Daniel Fernández, 2019. "Bayesian model-based clustering for longitudinal ordinal data," Computational Statistics, Springer, vol. 34(3), pages 1015-1038, September.
  34. Yupeng Chen & Raghuram Iyengar & Garud Iyengar, 2017. "Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis—A Sparse Learning Approach," Marketing Science, INFORMS, vol. 36(1), pages 140-156, January.
  35. Hu, Hao & Wu, Yichao & Yao, Weixin, 2016. "Maximum likelihood estimation of the mixture of log-concave densities," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 137-147.
  36. Bei Jiang & Michael R. Elliott & Mary D. Sammel & Naisyin Wang, 2015. "Joint modeling of cross-sectional health outcomes and longitudinal predictors via mixtures of means and variances," Biometrics, The International Biometric Society, vol. 71(2), pages 487-497, June.
  37. Sacco Chiara & Viroli Cinzia & Falchi Mario, 2017. "A statistical test for detecting parent-of-origin effects when parental information is missing," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(4), pages 275-289, September.
  38. Timothy D. Johnson, 2003. "Bayesian Deconvolution Analysis of Pulsatile Hormone Concentration Profiles," Biometrics, The International Biometric Society, vol. 59(3), pages 650-660, September.
  39. Richard J. Boys & Daniel A. Henderson, 2004. "A Bayesian Approach to DNA Sequence Segmentation," Biometrics, The International Biometric Society, vol. 60(3), pages 573-581, September.
  40. Amy LaLonde & Tanzy Love & Sally W. Thurston & Philip W. Davidson, 2020. "Discovering structure in multiple outcomes models for tests of childhood neurodevelopment," Biometrics, The International Biometric Society, vol. 76(3), pages 874-885, September.
  41. Borochin, Paul & Golec, Joseph, 2016. "Using options to measure the full value-effect of an event: Application to Obamacare," Journal of Financial Economics, Elsevier, vol. 120(1), pages 169-193.
  42. Xiang, Sijia & Yao, Weixin & Seo, Byungtae, 2016. "Semiparametric mixture: Continuous scale mixture approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 413-425.
  43. Xiaoyi Han & Lung-Fei Lee, 2016. "Bayesian Analysis of Spatial Panel Autoregressive Models With Time-Varying Endogenous Spatial Weight Matrices, Common Factors, and Random Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 642-660, October.
  44. Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
  45. Saldaña-Zepeda, Dayna P. & Velasco-Cruz, Ciro & Torres-Preciado, Víctor H., 2020. "Mexican peso-USD exchange rate: A switching linear dynamical model application," International Economics, Elsevier, vol. 162(C), pages 80-91.
  46. Panagiotis Papastamoulis & George Iliopoulos, 2013. "On the Convergence Rate of Random Permutation Sampler and ECR Algorithm in Missing Data Models," Methodology and Computing in Applied Probability, Springer, vol. 15(2), pages 293-304, June.
  47. Antonio Punzo & Salvatore Ingrassia, 2016. "Clustering bivariate mixed-type data via the cluster-weighted model," Computational Statistics, Springer, vol. 31(3), pages 989-1013, September.
  48. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
  49. Sinha, Shyamalendu & Hart, Jeffrey D., 2019. "Estimating the mean and variance of a high-dimensional normal distribution using a mixture prior," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 201-221.
  50. Rufo, M.J. & Pérez, C.J. & Martín, J., 2009. "Local parametric sensitivity for mixture models of lifetime distributions," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1238-1244.
  51. Michel Meulders & Paul Boeck & Iven Mechelen, 2003. "A taxonomy of latent structure assumptions for probability matrix decomposition models," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 61-77, March.
  52. Chuku Chuku & Paul Middleditch, 2020. "Characterizing Monetary and Fiscal Policy Rules and Interactions when Commodity Prices Matter," Manchester School, University of Manchester, vol. 88(3), pages 373-404, June.
  53. Jeong Eun Lee & Christian Robert, 2013. "Imortance Sampling Schemes for Evidence Approximation in Mixture Models," Working Papers 2013-42, Center for Research in Economics and Statistics.
  54. Richard Arnold & Yu Hayakawa & Paul Yip, 2010. "Capture–Recapture Estimation Using Finite Mixtures of Arbitrary Dimension," Biometrics, The International Biometric Society, vol. 66(2), pages 644-655, June.
  55. Luis Orea & Tooraj Jamasb, 2017. "Regulating Heterogeneous Utilities: A New Latent Class Approach with Application to the Norwegian Electricity Distribution Networks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
  56. Elena A. Erosheva & S. McKay Curtis, 2017. "Dealing with Reflection Invariance in Bayesian Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 295-307, June.
  57. Grn, Bettina & Leisch, Friedrich, 2009. "Dealing with label switching in mixture models under genuine multimodality," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 851-861, May.
  58. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Economics Working Papers 2012-11, Christian-Albrechts-University of Kiel, Department of Economics.
  59. Hui, Francis K.C., 2017. "Model-based simultaneous clustering and ordination of multivariate abundance data in ecology," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 1-10.
  60. Lee, Kuo-Jung & Feldkircher, Martin & Chen, Yi-Chi, 2021. "Variable selection in finite mixture of regression models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
  61. Jiang, Yu, 2020. "Identification of business cycles and the Great Moderation in the post-war U.S. economy," Economics Letters, Elsevier, vol. 190(C).
  62. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
  63. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.
  64. 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.
  65. Liqun Wang & James Fu, 2007. "A practical sampling approach for a Bayesian mixture model with unknown number of components," Statistical Papers, Springer, vol. 48(4), pages 631-653, October.
  66. Wan-Lun Wang & Tsung-I Lin, 2020. "Automated learning of mixtures of factor analysis models with missing information," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1098-1124, December.
  67. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Model-based Clustering of non-Gaussian Panel Data," MPRA Paper 880, University Library of Munich, Germany.
  68. De la Cruz-Mesia, Rolando & Quintana, Fernando A. & Marshall, Guillermo, 2008. "Model-based clustering for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1441-1457, January.
  69. Dong Soo Kim & Roger A. Bailey & Nino Hardt & Greg M. Allenby, 2017. "Benefit-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 36(1), pages 54-69, January.
  70. Bai, Xiuqin & Yao, Weixin & Boyer, John E., 2012. "Robust fitting of mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2347-2359.
  71. Oliver Rutz & Randolph Bucklin, 2012. "Does banner advertising affect browsing for brands? clickstream choice model says yes, for some," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 231-257, June.
  72. Ungolo, Francesco & Kleinow, Torsten & Macdonald, Angus S., 2020. "A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 68-84.
  73. Royce Anders & William Batchelder, 2015. "Cultural Consensus Theory for the Ordinal Data Case," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 151-181, March.
  74. Ntzoufras, Ioannis & Tarantola, Claudia, 2013. "Conjugate and conditional conjugate Bayesian analysis of discrete graphical models of marginal independence," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 161-177.
  75. Lin, L. & Fong, D.K.H., 2019. "Bayesian multidimensional scaling procedure with variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 1-13.
  76. Murakami, Junko, 2009. "Bayesian posterior mean estimates for Poisson hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 941-955, February.
  77. Arima, Serena & Basset, Alberto & Jona Lasinio, Giovanna & Pollice, Alessio & Rosati, Ilaria, 2013. "A hierarchical Bayesian model for the ecological status classification of lagoons," Ecological Modelling, Elsevier, vol. 263(C), pages 187-195.
  78. Mark Bognanni & Edward P. Herbst, 2014. "Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach," Working Papers (Old Series) 1427, Federal Reserve Bank of Cleveland.
  79. Ioannis Ntzoufras & Claudia Tarantola, 2012. "Conjugate and Conditional Conjugate Bayesian Analysis of Discrete Graphical Models of Marginal Independence," Quaderni di Dipartimento 178, University of Pavia, Department of Economics and Quantitative Methods.
  80. Gilles Celeux & Florence Forbes & Christian P, Robert & Michael Titterington, 2003. "Deviance Information Criteria for Missing Data Models," Working Papers 2003-30, Center for Research in Economics and Statistics.
  81. Edward Ip & Qiang Zhang & Jack Rejeski & Tammy Harris & Stephen Kritchevsky, 2013. "Partially Ordered Mixed Hidden Markov Model for the Disablement Process of Older Adults," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 370-384, June.
  82. Kim Jin Gyo & Menzefricke Ulrich & Feinberg Fred M., 2004. "Assessing Heterogeneity in Discrete Choice Models Using a Dirichlet Process Prior," Review of Marketing Science, De Gruyter, vol. 2(1), pages 1-41, January.
  83. Daniele Durante & Sally Paganin & Bruno Scarpa & David B. Dunson, 2017. "Bayesian modelling of networks in complex business intelligence problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 555-580, April.
  84. Yu Jiang & Xianming Fang, 2014. "Identify regimes in post-war US GDP growth," Applied Economics Letters, Taylor & Francis Journals, vol. 21(6), pages 397-401, April.
  85. Brian Hartley, 2020. "Corridor stability of the Kaleckian growth model: a Markov-switching approach," Working Papers 2013, New School for Social Research, Department of Economics, revised Nov 2020.
  86. Shiow-Lan Gau & Jean Dieu Tapsoba & Shen-Ming Lee, 2014. "Bayesian approach for mixture models with grouped data," Computational Statistics, Springer, vol. 29(5), pages 1025-1043, October.
  87. James C. Slaughter & Amy H. Herring & John M. Thorp, 2009. "A Bayesian Latent Variable Mixture Model for Longitudinal Fetal Growth," Biometrics, The International Biometric Society, vol. 65(4), pages 1233-1242, December.
  88. Chuan Zhou & Jon Wakefield, 2006. "A Bayesian Mixture Model for Partitioning Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 515-525, June.
  89. Paroli, Roberta & Spezia, Luigi, 2008. "Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2311-2330, January.
  90. Bansal, Prateek & Hurtubia, Ricardo & Tirachini, Alejandro & Daziano, Ricardo A., 2019. "Flexible estimates of heterogeneity in crowding valuation in the New York City subway," Journal of choice modelling, Elsevier, vol. 31(C), pages 124-140.
  91. Sarah E. Heaps & Malcolm Farrow & Kevin J. Wilson, 2020. "Identifying the effect of public holidays on daily demand for gas," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 471-492, February.
  92. Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
  93. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
  94. Komárek, Arnost, 2009. "A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3932-3947, October.
  95. Aßmann, Christian & Boysen-Hogrefe, Jens, 2009. "A bayesian approach to model-based clustering for panel probit models," Economics Working Papers 2009-03, Christian-Albrechts-University of Kiel, Department of Economics.
  96. Yuhong Wei & Paul McNicholas, 2015. "Mixture model averaging for clustering," 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. 9(2), pages 197-217, June.
  97. Chia-Yi Chiu & Yan Sun & Yanhong Bian, 2018. "Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 355-375, June.
  98. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
  99. Sylvia Frühwirth-Schnatter & Leopold Sögner, 2009. "Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma law," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 159-179, March.
  100. Jia-Chiun Pan & Chih-Min Liu & Hai-Gwo Hwu & Guan-Hua Huang, 2015. "Allocation Variable-Based Probabilistic Algorithm to Deal with Label Switching Problem in Bayesian Mixture Models," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-23, October.
  101. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2017. "Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 141-156.
  102. Jonathan Jaeger & Philippe Lambert, 2014. "Bayesian penalized smoothing approaches in models specified using differential equations with unknown error distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2709-2726, December.
  103. Jia-Chiun Pan & Guan-Hua Huang, 2014. "Bayesian Inferences of Latent Class Models with an Unknown Number of Classes," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 621-646, October.
  104. Oliver J. Rutz & Garrett P. Sonnier, 2019. "VANISH regularization for generalized linear models," Quantitative Marketing and Economics (QME), Springer, vol. 17(4), pages 415-437, December.
  105. Sijia Xiang & Weixin Yao, 2018. "Semiparametric mixtures of nonparametric regressions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 131-154, February.
  106. Michael Braun & Peter S. Fader & Eric T. Bradlow & Howard Kunreuther, 2006. "Modeling the "Pseudodeductible" in Insurance Claims Decisions," Management Science, INFORMS, vol. 52(8), pages 1258-1272, August.
  107. Simen Alexander Linge Johnsen & Jörg Bollmann, 2020. "Coccolith mass and morphology of different Emiliania huxleyi morphotypes: A critical examination using Canary Islands material," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-29, March.
  108. Nichole E. Carlson & Timothy D. Johnson & Morton B. Brown, 2009. "A Bayesian Approach to Modeling Associations Between Pulsatile Hormones," Biometrics, The International Biometric Society, vol. 65(2), pages 650-659, June.
  109. Benjamin E. Leiby & Mary D. Sammel & Thomas R. Ten Have & Kevin G. Lynch, 2009. "Identification of multivariate responders and non‐responders by using Bayesian growth curve latent class models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 505-524, September.
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