Large tests of independence in incomplete two-way contingency tables using fractional imputation

Abstract

Imputation procedures fill-in missing values, thereby enabling complete data analyses. Fully efficient fractional imputation (FEFI) and multiple imputation (MI) create multiple versions of the missing observations, thereby reflecting uncertainty about their true values. Methods have been described for hypothesis testing with multiple imputation. Fractional imputation assigns weights to the observed data to compensate for missing values. The focus of this article is the development of tests of independence using FEFI for partially classified two-way contingency tables. Wald and deviance tests of independence under FEFI are proposed. Simulations are used to compare type I error rates and Power. The partially observed marginal information is useful for estimating the joint distribution of cell probabilities, but it is not useful for testing association. FEFI compares favorably to other methods in simulations.

Variance estimation for distribution rate in stratified cluster sampling with missing values

“…There are many researches about handling nonresponses. We can see some recent researches in domestic, e.g., Woo and Kim (2016), Kang et al (2015), Park and Na (2014), etc.…”