Missing data estimation based on the chaining technique in survey sampling
Missing data estimation based on the chaining technique in survey sampling

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 c

Summary/Abstract: Sample surveys are often affected by missing observations and non-response caused by therespondents’ refusal or unwillingness to provide the requested information or due to theirmemory failure. In order to substitute the missing data, a procedure called imputation isapplied, 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 thesample. The aim of the paper is to present the application of the Chain-type factor estimatoras 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 similarestimation procedures described in the relevant literature. These techniques could also bemade nearly unbiased in relation to other selected parametric values. The findings aresupported by a numerical study involving the use of a dataset, proving that the proposedtechniques outperform other similar ones.

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