Non-Parametric Inference for Clustered Binary and Count Data when Only Summary Information is Available

DOI10.1111/j.1467-9868.2008.00658.xOpenAlexW1964751346MaRDI QIDQ3631469

Hall, Peter, Tapabrata Maiti

Publication date: 10 June 2009

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9868.2008.00658.x

zbMATH Keywords

predictionbandwidthsieve methoddeconvolutiontwo-stage designidentifiabilitymixed modelsmisspecificationlink functionrandom effectempirical predictorstratified designsmall area inference

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Deconvolution Methods for Non-Parametric Inference in two-level Mixed Models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Practical bandwidth selection in deconvolution kernel density estimation
• On Parametric Bootstrap Methods for Small Area Prediction
• Smooth Random Effects Distribution in a Linear Mixed Model
• A Smooth Nonparametric Estimate of a Mixing Distribution Using Mixtures of Gaussians
• Estimation of Integrated Squared Density Derivatives from a Contaminated Sample
• On the effect of misspecifying the error density in a deconvolution problem
• Approximate Inference in Generalized Linear Mixed Models
• Asymptotic Normality of Kernel-Type Deconvolution Estimators
• Applied Mixed Models in Medicine
• Sieve Maximum Likelihood Estimator for Semiparametric Regression Models With Current Status Data