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

Aamir Sanaullah

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

Aamir Sanaullah * §

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GC University, Lahore, Pakistan.

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

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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.

References

42 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Aamir Sanaullah. 2013. "A Class of Improved Estimators for Estimating Population Mean Regarding Partial Information in Double Sampling". 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

Keywords

auxiliary variable Bias double sampling Mean square error partial information

Classification

GJSFR-F Classification MSC 2010: 62F12

06A06

Submission ReceivedOctober 15, 2012
Peer Review Double Blind
Handling Editor
Accepted November 8, 2012
Published November 20, 2012

Version of record

v1.2

Issue date

January 5, 2013

Language

English

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Article Matrices

Total Score: 152

Country: Pakistan

Subject: Global Journal of Science Frontier Research

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

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