Multiple Group Comparisons of the Fixed and Random Effects From the Generalized Linear Mixed Model
Bibliographic Data
| ID | 2331040 |
|---|---|
| Authors | Daniel Kasper (0000-0002-2804-1256, Universität Hamburg, corresponding author), Katrin Schulz-Heidorf (Universität Hamburg), Knut Schwippert (0000-0002-4000-9726, Universität Hamburg) |
| Year | 2024 |
| Volume | 53 |
| Issue | 1 |
| Pages | 448-504 |
| Publication date | 2024-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methods & Research (JOURNAL) |
| Journal identifiers | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0049124120986182 |
| OpenAlex | W3133581650 |
| Language | EN |
| Citations received | 1 |
| References cited | 82 |
In this article, we extend Liao's test for across-group comparisons of the fixed effects from the generalized linear model to the fixed and random effects of the generalized linear mixed model (GLMM). Using as our basis the Wald statistic, we developed an asymptotic test statistic for across-group comparisons of these effects. The test can be applied when the fixed and random effects are multivariate normally distributed, and it works well for any link function and conditional distribution of the dependent variable of the GLMM. We also derived the asymptotic properties of this test, and because power information does not exist for either our new test statistic or Liao's test, we implemented a power study to demonstrate the superiority of these tests over the alternatively proposed F test. Using an example, we show the application of the test and then discuss its possible restrictions with respect to the distribution of the random effects
Asymptotic distribution · F-test · Fixed effects model · Generalized linear mixed model · Generalized linear model · Meta-analysis · Mixed model · Panel data · Random effects model · Statistic · Statistical hypothesis testing · Statistics · Test statistic · Wald test · Applied Mathematics · Genetics and Plant Breeding · Mathematics · Optimal Experimental Design Methods · Statistical Methods and Bayesian Inference
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |