Quantile regression for longitudinal data via the multivariate generalized hyperbolic distribution

被引：3

作者：

Florez, Alvaro J. ^{[1
,2
]}

Keilegom, Ingrid Van ^{[3
]}

Molenberghs, Geert ^{[1
,4
]}

Verhasselt, Anneleen ^{[1
]}

机构：

[1] Univ Hasselt, DSI, I BioStat, Hasselt, Belgium

[2] Univ Valle, Sch Stat, Cali, Colombia

[3] Katholieke Univ Leuven, ORSTAT, Naamsestr 69, B-3000 Leuven, Belgium

[4] Katholieke Univ Leuven, I BioStat, Leuven, Belgium

来源：

STATISTICAL MODELLING | 2022年 / 22卷 / 06期

基金：

欧洲研究理事会;

关键词：

asymptotics; Longitudinal data; maximum likelihood; pseudo-likelihood; quantile regression; BOOTSTRAP INFERENCE;

D O I：

10.1177/1471082X211015454

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

While extensive research has been devoted to univariate quantile regression, this is considerably less the case for the multivariate (longitudinal) version, even though there are many potential applications, such as the joint examination of growth curves for two or more growth characteristics, such as body weight and length in infants. Quantile functions are easier to interpret for a population of curves than mean functions. While the connection between multivariate quantiles and the multivariate asymmetric Laplace distribution is known, it is less well known that its use for maximum likelihood estimation poses mathematical as well as computational challenges. Therefore, we study a broader family of multivariate generalized hyperbolic distributions, of which the multivariate asymmetric Laplace distribution is a limiting case. We offer an asymptotic treatment. Simulations and a data example supplement the modelling and theoretical considerations.

引用

页码：566 / 584

页数：19

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