Missing data estimation based on the chaining technique in survey sampling - Narendra Singh Thakur; Diwakar Shukla (Statistics in Transition new series (SiTns), 2022)
Sample surveys are often affected by missing observations and non-response caused by the respondents' refusal or unwillingness to provide the requested information or due to their memory failure. In order to substitute the missing data, a procedure called imputation is applied, which uses the available data as a tool for the replacement of the missing values. Two auxiliary variables create a chain which is used to substitute the missing part of the sample. The aim of the paper is to present the application of the Chain-type factor estimator as a means of source imputation for the non-response units in an incomplete sample. The proposed strategies were found to be more efficient and bias-controllable than similar estimation procedures described in the relevant literature. These techniques could also be made nearly unbiased in relation to other selected parametric values. The findings are supported by a numerical study involving the use of a dataset, proving that the proposed techniques outperform other similar ones.
Keywords: 519; estimation; missing data; chaining; imputation; bias; mean squared error (MSE); factor type (F-T); chain type estimator; double sampling