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Marco Geraci

Personal Details

First Name:Marco
Middle Name:
Last Name:Geraci
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RePEc Short-ID:pge379
[This author has chosen not to make the email address public]

Affiliation

Dipartimento di Metodi e modelli per l'economia, il territorio e la finanza (MEMOTEF)
Facoltà di Economia
"Sapienza" Università di Roma

Roma, Italy
https://web.uniroma1.it/memotef/
RePEc:edi:dmrosit (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Alessio Farcomeni & Marco Geraci, 2022. "Alessio Farcomeni and Marco Geraci's contribution to the ‘First Discussion Meeting on Statistical Aspects of the Covid‐19 Pandemic’," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1829-1830, October.
  2. Marco Geraci, 2019. "Additive quantile regression for clustered data with an application to children's physical activity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(4), pages 1071-1089, August.
  3. Geraci, Marco, 2019. "Modelling and estimation of nonlinear quantile regression with clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 30-46.
  4. Marco Geraci & Alexander McLain, 2018. "Multiple Imputation for Bounded Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 919-940, December.
  5. Francesco Sera & Lucy J Griffiths & Carol Dezateux & Marco Geraci & Mario Cortina-Borja, 2017. "Using functional data analysis to understand daily activity levels and patterns in primary school-aged children: Cross-sectional analysis of a UK-wide study," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
  6. Hakim-Moulay Dehbi & Mario Cortina-Borja & Marco Geraci, 2016. "Aranda-Ordaz quantile regression for student performance assessment," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 58-71, January.
  7. Marco Geraci & Alessio Farcomeni, 2016. "Probabilistic principal component analysis to identify profiles of physical activity behaviours in the presence of non-ignorable missing data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 51-75, January.
  8. Geraci, Marco, 2014. "Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i13).
  9. Carly Rich & Marco Geraci & Lucy Griffiths & Francesco Sera & Carol Dezateux & Mario Cortina-Borja, 2014. "Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.
  10. Carly Rich & Marco Geraci & Lucy Griffiths & Francesco Sera & Carol Dezateux & Mario Cortina-Borja, 2013. "Quality Control Methods in Accelerometer Data Processing: Defining Minimum Wear Time," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
  11. Marco Geraci & M. Degli Esposti, 2011. "Where do Italian universities stand? An in-depth statistical analysis of national and international rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 667-681, June.

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.

Articles

  1. Geraci, Marco, 2019. "Modelling and estimation of nonlinear quantile regression with clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 30-46.

    Cited by:

    1. Bahram Adrangi & Arjun Chatrath & Madhuparna Kolay & Kambiz Raffiee, 2021. "Dynamic Responses of Standard and Poor’s Regional Bank Index to the U.S. Fear Index, VIX," JRFM, MDPI, vol. 14(3), pages 1-18, March.
    2. Xiaoming Lu & Zhaozhi Fan, 2020. "Generalized linear mixed quantile regression with panel data," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    3. Bahram Adrangi & Arjun Chatrath & Kambiz Raffiee, 2023. "S&P 500 volatility, volatility regimes, and economic uncertainty," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1362-1387, October.
    4. Aleida Cobas-Valdés & Javier Fernández-Macho, 2021. "Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability," Sustainability, MDPI, vol. 13(21), pages 1-12, October.

  2. Marco Geraci & Alexander McLain, 2018. "Multiple Imputation for Bounded Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 919-940, December.

    Cited by:

    1. Urko Aguirre-Larracoechea & Cruz E. Borges, 2021. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches," Mathematics, MDPI, vol. 9(17), pages 1-27, August.

  3. Francesco Sera & Lucy J Griffiths & Carol Dezateux & Marco Geraci & Mario Cortina-Borja, 2017. "Using functional data analysis to understand daily activity levels and patterns in primary school-aged children: Cross-sectional analysis of a UK-wide study," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.

    Cited by:

    1. Selene Yue Xu & Sandahl Nelson & Jacqueline Kerr & Suneeta Godbole & Eileen Johnson & Ruth E. Patterson & Cheryl L. Rock & Dorothy D. Sears & Ian Abramson & Loki Natarajan, 2019. "Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 403-421, July.
    2. Andy Daly-Smith & Matthew Hobbs & Jade L. Morris & Margaret A. Defeyter & Geir K. Resaland & Jim McKenna, 2021. "Moderate-to-Vigorous Physical Activity in Primary School Children: Inactive Lessons Are Dominated by Maths and English," IJERPH, MDPI, vol. 18(3), pages 1-14, January.

  4. Hakim-Moulay Dehbi & Mario Cortina-Borja & Marco Geraci, 2016. "Aranda-Ordaz quantile regression for student performance assessment," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 58-71, January.

    Cited by:

    1. Diego Ramos Canterle & Fábio Mariano Bayer, 2019. "Variable dispersion beta regressions with parametric link functions," Statistical Papers, Springer, vol. 60(5), pages 1541-1567, October.
    2. Cristine Rauber & Francisco Cribari-Neto & Fábio M. Bayer, 2020. "Improved testing inferences for beta regressions with parametric mean link function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 687-717, December.
    3. Marco Centoni & Vieri Del Panta & Antonello Maruotti & Valentina Raponi, 2019. "Concomitant-Variable Latent-Class Beta Inflated Models to Assess Students’ Performance: An Italian Case Study," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 7-18, November.
    4. Guilherme Pumi & Cristine Rauber & Fábio M. Bayer, 2020. "Kumaraswamy regression model with Aranda-Ordaz link function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1051-1071, December.
    5. Marco Geraci & Alexander McLain, 2018. "Multiple Imputation for Bounded Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 919-940, December.

  5. Marco Geraci & Alessio Farcomeni, 2016. "Probabilistic principal component analysis to identify profiles of physical activity behaviours in the presence of non-ignorable missing data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 51-75, January.

    Cited by:

    1. A. Iodice D’Enza & A. Markos & F. Palumbo, 2022. "Chunk-wise regularised PCA-based imputation of missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 365-386, June.
    2. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.

  6. Geraci, Marco, 2014. "Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i13).

    Cited by:

    1. Geraci, Marco, 2019. "Modelling and estimation of nonlinear quantile regression with clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 30-46.
    2. Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2020. "Brq: an R package for Bayesian quantile regression," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 313-328, December.
    3. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    4. Hakim-Moulay Dehbi & Mario Cortina-Borja & Marco Geraci, 2016. "Aranda-Ordaz quantile regression for student performance assessment," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 58-71, January.
    5. Kingwell, Ross S. & Xayavong, Vilaphonh, 2017. "How drought affects the financial characteristics of Australian farm businesses," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 61(3), July.
    6. Susana Faria & Maria Conceição Portela, 2016. "Student Performance in Mathematics using PISA-2009 data for Portugal," Working Papers de Gestão (Management Working Papers) 01, Católica Porto Business School, Universidade Católica Portuguesa.
    7. Sara Pereira & Flávio Bastos & Carla Santos & José Maia & Go Tani & Leah E. Robinson & Peter T. Katzmarzyk, 2022. "Variation and Predictors of Gross Motor Coordination Development in Azorean Children: A Quantile Regression Approach," IJERPH, MDPI, vol. 19(9), pages 1-13, April.
    8. Ruhai Bai & Junxiang Wei & Ruopeng An & Yan Li & Laura Collett & Shaonong Dang & Wanyue Dong & Duolao Wang & Zeping Fang & Yaling Zhao & Youfa Wang, 2018. "Trends in Life Expectancy and Its Association with Economic Factors in the Belt and Road Countries—Evidence from 2000–2014," IJERPH, MDPI, vol. 15(12), pages 1-11, December.
    9. Xiaoming Lu & Zhaozhi Fan, 2020. "Generalized linear mixed quantile regression with panel data," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    10. Raffaele Miniaci & Paolo Panteghini, 2021. "On the Capital Structure of Foreign Subsidiaries: Evidence from a Panel Data Quantile Regression Model," CESifo Working Paper Series 9085, CESifo.
    11. Koller, Manuel, 2016. "robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i06).
    12. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    13. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.

  7. Marco Geraci & M. Degli Esposti, 2011. "Where do Italian universities stand? An in-depth statistical analysis of national and international rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 667-681, June.

    Cited by:

    1. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    2. Mario Nicoliello, 2023. "Some Notes about Academic Evaluation: New Challenges for Accounting Scholars in Italy," International Journal of Business and Management, Canadian Center of Science and Education, vol. 16(11), pages 1-51, February.
    3. Francesca DE BATTISTI & Silvia SALINI, 2011. "Robust analysis of bibliometric data," Departmental Working Papers 2011-36, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    4. M. Benito & R. Romera, 2011. "Improving quality assessment of composite indicators in university rankings: a case study of French and German universities of excellence," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 153-176, October.
    5. Aparna Basu & Sumit Kumar Banshal & Khushboo Singhal & Vivek Kumar Singh, 2016. "Designing a Composite Index for research performance evaluation at the national or regional level: ranking Central Universities in India," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1171-1193, June.

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