Iterated imputation estimation for generalized linear models with missing response and covariate values

DOI10.1016/J.CSDA.2016.04.010zbMath1466.62063OpenAlexW2361809319MaRDI QIDQ1658989

Jun Shao, Fang Fang

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2016.04.010

zbMATH Keywords

maximum likelihoodimputationmissing at randommissing covariateiteration convergencearbitrary missing pattern

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (1)

Generating missing values for simulation purposes: a multivariate amputation procedure

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Parametric fractional imputation for missing data analysis
• Estimation and imputation in linear regression with missing values in both response and covariate
• Theory and inference for regression models with missing responses and covariates
• Analysis of multivariate missing data with nonignorable nonresponse
• Bias in Estimating Association Parameters for Longitudinal Binary Responses with Drop‐Outs
• Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Regression Analysis with Missing Covariate Data Using Estimating Equations
• A Weighted Estimating Equation for Missing Covariate Data with Properties Similar to Maximum Likelihood
• Adaptive Rejection Sampling for Gibbs Sampling
• Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random
• Missing-Data Methods for Generalized Linear Models

This page was built for publication: Iterated imputation estimation for generalized linear models with missing response and covariate values