Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/266305 
Year of Publication: 
2022
Citation: 
[Journal:] Statistics in Transition new series (SiTns) [ISSN:] 2450-0291 [Volume:] 23 [Issue:] 2 [Publisher:] Sciendo [Place:] Warsaw [Year:] 2022 [Pages:] 17-32
Publisher: 
Sciendo, Warsaw
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.
Subjects: 
adjusted imputation
jackknife variance estimators
linearized jackknife,missing values
winsorized variance
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Article

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