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An estimation method for non-response model using Monte-Carlo expectation-maximization algorithm
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 Title & Authors
An estimation method for non-response model using Monte-Carlo expectation-maximization algorithm
Choi, Boseung; You, Hyeon Sang; Yoon, Yong Hwa;
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 Abstract
In predicting an outcome of election using a variety of methods ahead of the election, non-response is one of the major issues. Therefore, to address the non-response issue, a variety of methods of non-response imputation may be employed, but the result of forecasting tend to vary according to methods. In this study, in order to improve electoral forecasts, we studied a model based method of non-response imputation attempting to apply the Monte Carlo Expectation Maximization (MCEM) algorithm, introduced by Wei and Tanner (1990). The MCEM algorithm using maximum likelihood estimates (MLEs) is applied to solve the boundary solution problem under the non-ignorable non-response mechanism. We performed the simulation studies to compare estimation performance among MCEM, maximum likelihood estimation, and Bayesian estimation method. The results of simulation studies showed that MCEM method can be a reasonable candidate for non-response model estimation. We also applied MCEM method to the Korean presidential election exit poll data of 2012 and investigated prediction performance using modified within precinct error (MWPE) criterion (Bautista et al., 2007).
 Keywords
Generalized linear model;imputation;MCEM;missing data;non-ignorable non-response;
 Language
Korean
 Cited by
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