Power Calculations for Likelihood Ratio Tests in Generalized Linear Models

Biometrics ◽  
1992 ◽  
Vol 48 (1) ◽  
pp. 31 ◽  
Author(s):  
Steven G. Self ◽  
Robert H. Mauritsen ◽  
Jill Ohara
Keyword(s):  
Likelihood Ratio ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Likelihood Ratio Tests ◽  
Power Calculations

scholarly journals Hypothesis Testing in Generalized Linear Models with Functional Coefficient Autoregressive Processes

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
Lei Song ◽  
Hongchang Hu ◽  
Xiaosheng Cheng
Keyword(s):  
Maximum Likelihood ◽  
Hypothesis Testing ◽  
Likelihood Ratio ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Autoregressive Processes ◽  
Pseudo Likelihood ◽  
Quasi Maximum Likelihood ◽  
Functional Coefficient

The paper studies the hypothesis testing in generalized linear models with functional coefficient autoregressive (FCA) processes. The quasi-maximum likelihood (QML) estimators are given, which extend those estimators of Hu (2010) and Maller (2003). Asymptotic chi-squares distributions of pseudo likelihood ratio (LR) statistics are investigated.

scholarly journals Pooling methods for likelihood ratio tests in multiply imputed data sets

2021 ◽  
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Likelihood Ratio ◽  
Statistical Models ◽  
Structural Equation ◽  
Linear Models ◽  
R Package ◽  
Likelihood Ratio Tests ◽  
Equation Modeling ◽  
Data Sets ◽  
New Methods ◽  
Multiply Imputed

Likelihood ratio tests (LRTs) are a popular tool for comparing statistical models. However, missing data are also common in empirical research, and multiple imputation (MI) is often used to deal with them. In multiply imputed data, there are multiple options for conducting LRTs, and new methods are still being proposed. In this article, we compare all available methods in multiple simulations covering applications in linear regression, generalized linear models, and structural equation modeling (SEM). In addition, we implemented these methods in an R package, and we illustrate its application in an example analysis concerned with the investigation of measurement invariance.

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