Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/8057
Title: | A family of tests to detect misspecifications in the random-effects structure of generalized linear mixed models | Authors: | ALONSO ABAD, Ariel LITIERE, Saskia MOLENBERGHS, Geert |
Issue Date: | 2008 | Publisher: | Elsevier | Source: | COMPUTATIONAL STATISTICS & DATA ANALYSIS, 52(9). p. 4474-4486 | Abstract: | Estimation in generalized linear mixed models for non-Gaussian longitudinal data is often based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. It is known that the results obtained from these models are not always robust against misspecification of the random-effects structure. Therefore, diagnostic tools for the detection of this misspecification are of the utmost importance. Three diagnostic tests, based on the eigenvalues of the variance-covariance matrices for the fixed-effects parameters estimates, are proposed in the present work. The power and type I error rate of these tests are studied via simulations. A very acceptable performance was observed in many cases, especially for those misspecifications that can have a big impact on the maximum likelihood estimators. | Keywords: | Generalized linear mixed models; Misspecification; Power; Type I | Document URI: | http://hdl.handle.net/1942/8057 | ISSN: | 0167-9473 | e-ISSN: | 1872-7352 | DOI: | 10.1016/j.csda.2008.02.033 | ISI #: | 000257014000022 | Rights: | (c) 2008 Elsevier B.V. All rights reserved. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2009 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
papereigen07.pdf | Peer-reviewed author version | 227.34 kB | Adobe PDF | View/Open |
a.pdf Restricted Access | Published version | 381.41 kB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
27
checked on Sep 3, 2020
WEB OF SCIENCETM
Citations
30
checked on Oct 12, 2024
Page view(s)
92
checked on Sep 7, 2022
Download(s)
136
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.