Revisiting the analysis pipeline for overdispersed Poisson and binomial data
From MaRDI portal
Publication:6134397
DOI10.1080/02664763.2022.2026897MaRDI QIDQ6134397
Jeonghwan Kim, Woo-Joo Lee, Unnamed Author
Publication date: 25 July 2023
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://figshare.com/articles/journal_contribution/Revisiting_the_analysis_pipeline_for_overdispersed_Poisson_and_binomial_data/18763482
Cites Work
- Unnamed Item
- Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function
- Random effect and latent variable model selection
- Overdispersion: Models and estimation.
- Order-restricted score tests for homogeneity in generalised linear and nonlinear mixed models
- The Use of Score Tests for Inference on Variance Components
- Statistical Analysis of Financial Data in S-Plus
- An Introduction to Categorical Data Analysis
- A locally most powerful test for homogeneity with many strata
- Negative binomial and mixed poisson regression
- On the use of nonparametric regression for model checking
- Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
- A comparison of tests for overdispersion in generalized linear models
- Variance component testing in generalised linear models with random effects
- Approximate Inference in Generalized Linear Mixed Models
- Some remarks on overdispersion
- A Score Test Against One-Sided Alternatives
- Test of Homogeneity of Binary Data with Explanatory Variables
- Testing the Fit of a Regression Model Via Score Tests in Random Effects Models
- Regression Analysis of Count Data
- Testing Approaches for Overdispersion in Poisson Regression versus the Generalized Poisson Model
- Bootstrap tests for variance components in generalized linear mixed models
- Recovery of inter-block information when block sizes are unequal
This page was built for publication: Revisiting the analysis pipeline for overdispersed Poisson and binomial data