Missing data estimation based on the chaining technique in survey sampling

Thakur Narendra Singh () and Shukla Diwakar ()
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Thakur Narendra Singh: Govt. Adarsh Girls College, Sheopur, (M.P.), India, Pin – 476337, Affiliation with Jiwaji University, Gwalior, (M.P.), India .
Shukla Diwakar: Dr. Harisingh Gour Central University, Sagar, (M.P.), India, Pin – 470003 .

Statistics in Transition New Series, 2022, vol. 23, issue 4, 91-111

Abstract: 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: estimation; missing data; chaining; imputation; bias; mean squared error (MSE); factor type (F-T); chain type estimator; double sampling; 62D05 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:23:y:2022:i:4:p:91-111:n:4

DOI: 10.2478/stattrans-2022-0044

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