Imputation for statistical inference with coarse data
Copyright policy )Imputation for statistical inference with coarse data
AbstractCoarse data is a general type of incomplete data that includes grouped data, censored data, and missing data. The likelihood‐based estimation approach with coarse data is challenging because the likelihood function is in integral form. The Monte Carlo EM algorithm of Wei & Tanner [Wei & Tanner (1990).Journal of the American Statistical Association, 85, 699–704] is adapted to compute the maximum likelihood estimator in the presence of coarse data. Stochastic coarse data is also covered and the computation can be implemented using the parametric fractional imputation method proposed by Kim [Kim (2011).Biometrika, 98, 119–132]. Results from a limited simulation study are presented. The proposed method is also applied to the Korean Longitudinal Study of Aging (KLoSA).The Canadian Journal of Statistics40: 604–618; 2012 © 2012 Statistical Society of Canada
- Iowa State University United States
grouped data, Sampling theory, sample surveys, Nonparametric statistical resampling methods, measurement error models, EM algorithm, Applications of statistics to biology and medical sciences; meta analysis, fractional imputation
grouped data, Sampling theory, sample surveys, Nonparametric statistical resampling methods, measurement error models, EM algorithm, Applications of statistics to biology and medical sciences; meta analysis, fractional imputation
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