Imputation based mean estimators in case of missing data utilizing robust regression and variance–covariance matrices

DOI10.1080/03610918.2020.1740266OpenAlexW3011723021MaRDI QIDQ5867463

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Publication date: 14 September 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2020.1740266

zbMATH Keywords

missing datarobust regressionsimple random samplingimputation methodsrelative mean square errorrobust variance-covariance matrices

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Novel log type class of estimators under ranked set sampling ⋮ Comparative study of different imputation methods ⋮ A novel family of variance estimators based on L-moments and calibration approach under stratified random sampling ⋮ New robust class of estimators for population mean under different sampling designs ⋮ Some efficient classes of estimators under stratified sampling ⋮ Estimation of finite population mean under probability-proportional-to-size sampling in the presence of extreme values

Uses Software

• SDaA

Cites Work

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• Inference and missing data
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• Modified regression estimators using robust regression methods and covariance matrices in stratified random sampling
• Modified ratio estimators using robust regression methods
• Robust-regression-type estimators for improving mean estimation of sensitive variables by using auxiliary information
• A new class of ratio-type estimators for improving mean estimation of nonsensitive and sensitive variables by using supplementary information
• On improvement in estimating population mean in stratified random sampling
• Robust Estimation of a Location Parameter
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