An improved class of estimators for finite population variance

Mazhar YAQUB; Javid SHABBİR

Article Details

An improved class of estimators for finite population variance

Authors:

Mazhar YAQUB | Javid SHABBİR

Publisher:

Hacettepe University

Main Topics:

Analysis

Journal:

Hacettepe Journal of Mathematics and Statistics 2016 Vol.45 Issue 5, pp.1641-1660

Document Type:

Article

Publication Date:

9/2016

Document Language:

English

Document ID (Org No.):

2000452509

Frequency:

Bimonthly

eISSN:

2651-477X

pISSN:

2651-477X

Abstract:

 

We propose an improved class of estimators in estimating the finite population variance, using the auxiliary information. The expressions for the bias and mean squared error of the proposed class of estimators are derived up to the first order of approximation. Some estimators are also derived from a proposed class by allocating the suitable values of known parameters and identified as particular members of the proposed class of estimators. A numerical study is carried out to demonstrate performances of the estimators. It is observed that the proposed class of estimators is more efficient than the usual sample mean estimator, the regression estimator suggested by Isaki (1983), Shabbir and Gupta (2007), Singh and Solanki (2013b), Yadav et al. (2013), Yadav and Kadilar (2014) and Singh and Malik (2014) estimators.

Article Details

An improved class of estimators for finite population variance

Authors: Mazhar YAQUB | Javid SHABBİR

Publisher: Hacettepe University

Main Topics: Analysis

Journal: Hacettepe Journal of Mathematics and Statistics 2016 Vol.45 Issue 5, pp.1641-1660

Document Type: Article

Publication Date: 9/2016

Document Language: English

Document ID (Org No.): 2000452509

Frequency: Bimonthly

eISSN: 2651-477X

pISSN: 2651-477X

Issues

Authors:

Mazhar YAQUB | Javid SHABBİR

Publisher:

Hacettepe University

Main Topics:

Analysis

Journal:

Hacettepe Journal of Mathematics and Statistics 2016 Vol.45 Issue 5, pp.1641-1660

Document Type:

Article

Publication Date:

9/2016

Document Language:

English

Document ID (Org No.):

2000452509

Frequency:

Bimonthly

eISSN:

2651-477X

pISSN:

2651-477X