Estimation of Population Mean Using Some Improved Imputation Methods for Missing Data in Sample Surveys
Erişim
info:eu-repo/semantics/openAccessTarih
2024Erişim
info:eu-repo/semantics/openAccessÜst veri
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Scopus EXPORT DATE: 13 August 2024 @ARTICLE{Pandey2024, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199878087&doi=10.1080%2f03610926.2024.2369314&partnerID=40&md5=e6c552c2c9ca82d9b1dc25b6e2bbe4b0}, affiliations = {Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India; Faculty of Health Sciences, Gumushane University, Gumushane, Turkey}, correspondence_address = {M.K. Pandey; Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India; email: maheshbabu3797@gmail.com}, publisher = {Taylor and Francis Ltd.}, issn = {03610926}, coden = {CSTMD}, language = {English}, abbrev_source_title = {Commun Stat Theory Methods} }Özet
Abstract.: In this research article, we present novel imputation methods designed to address missing data challenges in sample surveys. We then introduce innovative estimation procedures for calculating population means based on these methods. Our study thoroughly examines the properties of these new estimation procedures, assessing their biases and mean square errors. Through the use of both real and simulated data sets, we demonstrate the superior performance of our proposed estimators compared to existing methods in similar scenarios. In conclusion, we offer practical recommendations for survey practitioners. © 2024 Taylor & Francis Group, LLC.
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https://www.scopus.com/record/display.uri?eid=2-s2.0-85199878087&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=f77d82ded1a66e1eb52fbbb34e6e3560https://www.tandfonline.com/doi/pdf/10.1080/03610926.2024.2369314?needAccess=true
https://hdl.handle.net/20.500.12440/6283
