Jackknife variance estimation under two-phase sampling
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Title |
Jackknife variance estimation under two-phase sampling
Not Available |
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Creator |
V. Ramasubramanian
Anil Rai Randhir Singh |
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Subject |
Bootstrap
conditional inference design-based inference Jackknife simulation |
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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 |
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Date |
2022-02-14T06:34:29Z
2022-02-14T06:34:29Z 2007-05-22 |
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Type |
Research Paper
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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 |
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Language |
English
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Relation |
Not Available;
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Publisher |
IOS Press
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