Jackknife winsorized variance estimator under imputed data

Sohil Fariha (), Sohail Muhammad Umair (), Shabbir Javid () and Gupta Sat
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Sohil Fariha: Department of Education, The Women University, Multan, Pakistan .
Sohail Muhammad Umair: Department of Statistics, University of Narowal, Narowal, Pakistan .
Shabbir Javid: Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan .
Gupta Sat: Department of Mathematics and Statistics, University of North Carolina, Greensboro, USA .

Statistics in Transition New Series, 2022, vol. 23, issue 2, 17-32

Abstract: In the present study, we consider the problem of missing and extreme values for the estimation of population variance. The presence of extreme values either in the study variable, or the auxiliary variable, or in both of them, can adversely affect the performance of the estimation procedure. We consider three different situations for the presence of extreme values and also consider jackknife variance estimators for the population variance by handling these extreme values under stratified random sampling. Bootstrap technique ABB is carried out to understand the relative relationship more precisely.

Keywords: adjusted imputation; jackknife variance estimators; linearized jackknife; missing values; winsorized variance (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:2:p:17-32:n:8

DOI: 10.2478/stattrans-2022-0014

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