Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/34776
Title: | Quantile regression for longitudinal data via the multivariate generalized hyperbolic distribution | Authors: | FLOREZ POVEDA, Alvaro VAN KEILEGOM, Ingrid MOLENBERGHS, Geert VERHASSELT, Anneleen |
Issue Date: | 2022 | Publisher: | SAGE PUBLICATIONS LTD | Source: | Statistical modelling, 22 (6), p. 566-584 | Abstract: | 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. | Other: | Supplementary materials The R-code for executing the simulations and the data analysis is available at http://www.statmod.org/smij/archive.html. Additional results and technical details are exhibited in the Supplementary Materials available online (http://www.statmod.org/smij/archive.html). In Section A, an example of a multivariate longitudinal setting is introduced. Sections B-E show additional results of the simulation study. Finally, a sensitivity analysis of the MLE for selecting using the LDP and simulated data is presented in Section F. Sections G and H are related to the MAL distribution and to the asymptotic theory for the proposed estimator, respectively. | Keywords: | asymptotics;Longitudinal data;maximum likelihood;pseudo-likelihood;quantile regression | Document URI: | http://hdl.handle.net/1942/34776 | ISSN: | 1471-082X | e-ISSN: | 1477-0342 | DOI: | 10.1177/1471082X211015454 | ISI #: | 000660644500001 | Rights: | 2021 The Author(s) | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1471082x211015454.pdf Restricted Access | Published version | 894.17 kB | Adobe PDF | View/Open Request a copy |
WEB OF SCIENCETM
Citations
1
checked on Oct 14, 2024
Page view(s)
42
checked on Jul 5, 2022
Download(s)
16
checked on Jul 5, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.