This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behaviour.

Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach / Merlo, L.; Maruotti, A.; Petrella, L.. - In: STATISTICAL MODELLING. - ISSN 1471-082X. - (2021). [10.1177/1471082X21993603]

Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach

Merlo L.
;
Petrella L.
2021

Abstract

This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behaviour.
2021
correlated random effect models; LASSO; Nonparametric ML estimation; quantile regression mixture models; semi-continuous longitudinal data; two-part models
01 Pubblicazione su rivista::01a Articolo in rivista
Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach / Merlo, L.; Maruotti, A.; Petrella, L.. - In: STATISTICAL MODELLING. - ISSN 1471-082X. - (2021). [10.1177/1471082X21993603]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1552287
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