Academic Journal
A Monte Carlo permutation procedure for testing variance components in generalized linear regression models
| Title: | A Monte Carlo permutation procedure for testing variance components in generalized linear regression models |
|---|---|
| Authors: | Yahia S. El-Horbaty |
| Source: | Computational Statistics. 39:2605-2621 |
| Publisher Information: | Springer Science and Business Media LLC, 2023. |
| Publication Year: | 2023 |
| Subject Terms: | Statistics and Probability, Resampling, Permutation (music), Linear model, Social Sciences, Experimental Design and Optimization Methods, Management Science and Operations Research, 01 natural sciences, Decision Sciences, Statistical hypothesis testing, Methods for Handling Missing Data in Statistical Analysis, Variance (accounting), Accounting, 0502 economics and business, FOS: Mathematics, Business, 0101 mathematics, Linear regression, Statistic, Principal Component Analysis, Physics, 4. Education, 05 social sciences, Statistics, Acoustics, Type I and type II errors, Computer science, Monte Carlo method, Algorithm, Test statistic, Physical Sciences, Mathematics, Detection and Handling of Multicollinearity in Regression Analysis |
| Description: | Testing zero variance components is of utmost importance in various applications empowered by the use of mixed-effects models. Focusing on generalized linear models, this article proposes a permutation test using an analogue of the ANOVA test statistic that merely requires fitting the null model with independent observations. Monte Carlo simulations reveal that the new test has correct Type-I error rate and that its power compares favorably to an existing bootstrap score test. A real data application illustrates the advantageous capability of the proposed test in detecting the need for random effects. |
| Document Type: | Article Other literature type |
| Language: | English |
| ISSN: | 1613-9658 0943-4062 |
| DOI: | 10.1007/s00180-023-01403-y |
| DOI: | 10.60692/ag55y-63h63 |
| DOI: | 10.60692/k3t2c-2d012 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....6ecf52a5c7bea509b5849980e1afd3af |
| Database: | OpenAIRE |
| ISSN: | 16139658 09434062 |
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| DOI: | 10.1007/s00180-023-01403-y |