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Title: Semiparametric fractional imputation using conditional Gaussian mixture models Authors:  Jae Kwang Kim - Iowa State University (United States) [presenting]
Abstract: Imputation is a popular technique for handling item nonresponse in survey sampling. Semiparametric imputation is a robust imputation method that is based on a flexible model where the number of parameters in the model can increase with the sample size. Gaussian mixture model (GMM) imputation is one of the examples of the semiparametric imputation. We propose another semiparametric imputation based on a more flexible model assumption than the GMM. In the proposed mixture model, we still assume a Gaussian model for the conditional distribution of the study variable given the auxiliary variable, but the marginal distribution of the auxiliary variable is not necessarily Gaussian. We show that the proposed mixture model based on the conditional Gaussian mixture achieves a lower approximation error bound to any unknown target density than the GMM in terms of the Kullback-Leibler divergence measure. The proposed method is applicable to high dimensional covariate problems by including a penalty function in the conditional log-likelihood function. The parameter estimation computation can be efficiently implemented using a version of EM algorithm. The proposed method is applied to handle the real data problem in 2017 Korean Household Income and Expenditure Survey (KHIES) conducted by Statistics Korea.