Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/301891 
Year of Publication: 
2022
Citation: 
[Journal:] Statistics in Transition new series (SiTns) [ISSN:] 2450-0291 [Volume:] 23 [Issue:] 4 [Year:] 2022 [Pages:] 91-111
Publisher: 
Sciendo, Warsaw
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.
Subjects: 
estimation
missing data
chaining
imputation
bias
mean squared error (MSE)
factor type (F-T)
chain type estimator
double sampling
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Article

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