Group Testing Regression Models with Fixed and Random Effects

DOI10.1111/j.1541-0420.2008.01183.xzbMath1180.62160OpenAlexW2162150549WikidataQ37480327 ScholiaQ37480327MaRDI QIDQ5850977

Joshua M. Tebbs, Peng Chen, Christopher R. Bilder

Publication date: 21 January 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2794992

zbMATH Keywords

likelihood ratio testscore testgeneralized linear mixed modelMonte Carlo EM algorithmpooled testinglatent binary response

Mathematics Subject Classification ID

Related Items (13)

Pooling Designs for Outcomes under a Gaussian Random Effects Model ⋮ Statistical modeling for practical pooled testing during the COVID-19 pandemic ⋮ A semi-local likelihood regression estimator of the proportion based on group testing data ⋮ groupTesting: an R package for group testing estimation ⋮ The efficient design of Nested Group Testing algorithms for disease identification in clustered data ⋮ Group Testing Regression Analysis with Missing Data and Imperfect Tests ⋮ Nonparametric regression with homogeneous group testing data ⋮ Estimation of Conditional Prevalence From Group Testing Data With Missing Covariates ⋮ Sequential estimation in the group testing problem ⋮ Determination of varying group sizes for pooling procedure ⋮ Estimating disease prevalence using inverse binomial pooled testing ⋮ Regression models for group testing: identifiability and asymptotics ⋮ Evaluation of a Frequentist Hierarchical Model to Estimate Prevalence When Sampling from a Large Geographic Area Using Pool Screening

Cites Work

• Unnamed Item
• Unnamed Item
• Convergence rate of MLE in generalized linear and nonlinear mixed-effects models: Theory and applications
• Bias, efficiency, and agreement for group-testing regression models
• A locally most powerful test for homogeneity with many strata
• Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests Under Nonstandard Conditions
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Variance component testing in generalised linear models with random effects
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• Efficient estimation of the prevalence of multiple rare traits
• Analysis of Multistage Pooling Studies of Biological Specimens for Estimating Disease Incidence and Prevalence
• Regression Models for Disease Prevalence with Diagnostic Tests on Pools of Serum Samples
• A Score Test Against One-Sided Alternatives
• Bayesian Methods for Predicting Interacting Protein Pairs Using Domain Information
• A Monte Carlo EM algorithm for generalized linear mixed models with flexible random effects distribution

This page was built for publication: Group Testing Regression Models with Fixed and Random Effects