Journal of Statistical Theory and Applications

Volume 16, Issue 4, December 2017, Pages 565 - 575

A Class of Exponential Regression Type Estimators for Population Variance in Two-Phase Sampling

Authors
A. Chatterjee, G.N. Singh, A. Bandyopadhyay
Corresponding Author
A. Chatterjee
Received 13 March 2012, Accepted 17 December 2016, Available Online 1 December 2017.
DOI
10.2991/jsta.2017.16.4.10How to use a DOI?
Keywords
Double sampling, study variable, auxiliary variable, chain-type, Exponential, regression, bias, variance, efficiency.
Abstract

This article deals with the problems of efficient estimation of population variance in two-phase (double) sampling. Using information on two auxiliary variables, a class of chain exponential to regression type estimators has been proposed and its properties are studied under two different structures of two-phase sampling. Superiority of suggested class of estimators over some existing ones is established through numerical illustrations. Suitable recommendations to the survey statistician are also made.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Journal
Journal of Statistical Theory and Applications
Volume-Issue
16 - 4
Pages
565 - 575
Publication Date
2017/12/01
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
10.2991/jsta.2017.16.4.10How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - A. Chatterjee
AU  - G.N. Singh
AU  - A. Bandyopadhyay
PY  - 2017
DA  - 2017/12/01
TI  - A Class of Exponential Regression Type Estimators for Population Variance in Two-Phase Sampling
JO  - Journal of Statistical Theory and Applications
SP  - 565
EP  - 575
VL  - 16
IS  - 4
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2017.16.4.10
DO  - 10.2991/jsta.2017.16.4.10
ID  - Chatterjee2017
ER  -