Imputation using markov chains

DOI10.1080/00949658808811085zbMath0726.62017OpenAlexW2000038887MaRDI QIDQ3350470

Kim-Hung Li

Publication date: 1988

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949658808811085

zbMATH Keywords

simulationimputationpredictive distribution

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Inference from stochastic processes (62M99)

Related Items (12)

Multiple Imputation: Theory and Method ⋮ Markov-normal analysis of iterative simulations before their convergence ⋮ Bayesian estimation and model comparison for linear dynamic panel models with missing values ⋮ From EM to data augmentation: the emergence of MCMC Bayesian computation in the 1980s ⋮ Equi-energy sampler with applications in statistical inference and statistical mechanics ⋮ On computing the largest fraction of missing information for the EM algorithm and the worst linear function for data augmentation. ⋮ Impact of imputation of missing values on classification error for discrete data ⋮ Imputation in High-Dimensional Economic Data as Applied to the Agricultural Resource Management Survey ⋮ Multiple imputation of missing data with ante-dependence covariance structure ⋮ A Markov chain Monte Carlo algorithm for multiple imputation in large surveys ⋮ Survival Analysis Using Auxiliary Variables Via Multiple Imputation, with Application to AIDS Clinical Trial Data ⋮ Working with missing data: imputation of nonresponse items in categorical survey data with a non-monotone missing pattern

Cites Work

• Unnamed Item
• Unnamed Item
• Is victimization chronic? A Bayesian analysis of multinomial missing data
• ESTIMATION OF PARAMETERS OF MIXED EXPONENTIALLY DISTRIBUTED FAILURE TIME DISTRIBUTIONS FROM CENSORED LIFE TEST DATA
• Bayesian Estimators of the Parameters and Reliability Function from Mixed Exponentially Distributed Time-Censored Life Test Data
• The Calculation of Posterior Distributions by Data Augmentation
• Inference and missing data
• Monte Carlo sampling methods using Markov chains and their applications

This page was built for publication: Imputation using markov chains