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Missing data estimation based on the chaining technique in survey sampling

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Singh Thakur N., Shukla D.

Statistics in Transition new series

2022

vol. 23

issue 4

91-111

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.

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1 Statistics in Transition new series

1 Shukla D.

1 Singh Thakur N.

1 2022

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Missing data estimation based on the chaining technique in survey sampling