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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:

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    2. 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.
    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. 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).
    5. 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.
    6. 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. Geraci, Marco, 2019. "Modelling and estimation of nonlinear quantile regression with clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 30-46.
    8. 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.
    9. 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.
    10. Wright, Stephen E., 2024. "A note on computing maximum likelihood estimates for the three-parameter asymmetric Laplace distribution," Applied Mathematics and Computation, Elsevier, vol. 464(C).
    11. 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.
    12. 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.
    13. 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.
    14. 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.

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