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Jackknife variance estimation under two-phase sampling

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Title Jackknife variance estimation under two-phase sampling
Not Available
 
Creator V. Ramasubramanian
Anil Rai
Randhir Singh
 
Subject Bootstrap
conditional inference
design-based inference
Jackknife
simulation
 
Description Not Available
Two new Jackknife methods, as the counterparts of two existing Bootstrap methods of variance estimation under two-phase sampling, have been proposed. A simulation study has been conducted under both design-based and Conditional inference frameworks by generating two-phase samples from an infinite population for comparison of the proposed methods with five existing Jackknife and Bootstrap methods. The first method, the two-phase post-stratified Jackknife, reduces to an existing Jackknife variance estimation method considered under sampling from infinite population set up. The performance of the second method, the two-phase proportionate Jackknife, was better than two existing Jackknife methods while performing at par with another Jackknife method as well as with the two Bootstrap methods considered.
Not Available
 
Date 2022-02-14T06:34:29Z
2022-02-14T06:34:29Z
2007-05-22
 
Type Research Paper
 
Identifier V. Ramasubramanian, Anil Rai and Randhir Singh (2007). Jackknife variance estimation under two-phase sampling. Model Assisted Statistics and Applications, 2(1), 27-35.
https://content.iospress.com/articles/model-assisted-statistics-and-applications/mas00041
http://krishi.icar.gov.in/jspui/handle/123456789/69789
 
Language English
 
Relation Not Available;
 
Publisher IOS Press