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Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression

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  • Geraci, Marco

Abstract

Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a flexible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or longitudinal, which induce some form of clustering. In this paper, I will show how to estimate conditional quantile functions with random effects using the R package lqmm. Modeling, estimation and inference are discussed in detail using a real data example. A thorough description of the optimization algorithms is also provided.

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  • 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).
    • Handle: RePEc:jss:jstsof:v:057:i13
    DOI: http://hdl.handle.net/10.18637/jss.v057.i13
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    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.

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