A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling

A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling

Article ID

L81YR

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A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling

Dr. Saleha Shouket

Hina Khan

Aamir Sanaullah GC University, Lahore, Pakistan.

DOI

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Abstract

In this paper a class of improved estimators has been proposed for estimating population mean in two phase (double) sampling when only partial information is available on either of two auxiliary variables. Under simple random sampling (SRWOR), expressions of mean square error and bias have been derived to make comparison of suggested class with wide range of other estimators. Empirical study has also been given using five different natural populations. Empirical study confirmed that the suggested class of improved estimators is more efficient under percent relative efficiency (PRE) criterion.

A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling

Authors

Dr. Saleha Shouket

Hina Khan

Aamir Sanaullah GC University, Lahore, Pakistan.

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How to Cite

Aamir Sanaullah. 2013. “. Global Journal of Science Frontier Research – F: Mathematics & Decision GJSFR-F Volume 12 (GJSFR Volume 12 Issue F14): .

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

GJSFR Volume 12 Issue F14
Pg. 33- 45

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Submission Received October 15, 2012
Accepted November 8, 2012
Published November 20, 2012

Keywords

auxiliary variable Bias double sampling Mean square error partial information

Article Matrices

Total Score: 108

Country: Pakistan

Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision

Authors: Dr. Saleha Shouket, Hina Khan, Aamir Sanaullah (PhD/Dr. count: 1)

View Count (all-time): 105

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Total Downloads (simulated): 2634

Publish Date: 2013 01, Sat

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Total Views: 4965

Total Downloads: 2634

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