# Admissibility of the usual estimators under error-in-variables superpopulation model

*Guohua Zou* and
*Hua Liang*

*Statistics & Probability Letters*, 1997, vol. 32, issue 3, 301-309

**Abstract:**
In this paper, we first point out that a result in Mukhopadhyay (1994) on the optimality of the usual estimator sy2 of finite population variance is not true. We then give a necessary and sufficient condition for ((1 - f)/n) sy2 (where f means the sampling fraction) as the estimator of the precision of the sample mean s to be admissible in the class of quadratic estimators. Our result shows that there is virtual difference between the admissibility of estimators under error-in-variables superpopulation model and the usual superpopulation model. We also show that the improved estimator ((1 - f)/n) ((n - 1)/(n + 1)) sy2 over ((1 - f)/n) sy2 under the usual superpopulation model without measurement errors is admissible in the class of quadratic estimators.

**Keywords:** Superpopulation; model; Measurement; error; Quadratic; estimator; Admissibility (search for similar items in EconPapers)

**Date:** 1997

**References:** View complete reference list from CitEc

**Citations:** Track citations by RSS feed

**Downloads:** (external link)

http://www.sciencedirect.com/science/article/pii/S0167-7152(96)00087-9

Full text for ScienceDirect subscribers only

**Related works:**

This item may be available elsewhere in EconPapers: Search for items with the same title.

**Export reference:** BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text

**Persistent link:** https://EconPapers.repec.org/RePEc:eee:stapro:v:32:y:1997:i:3:p:301-309

**Ordering information:** This journal article can be ordered from

http://www.elsevier.com/wps/find/supportfaq.cws_home/regional

https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by *Somnath Datta* and *Hira L. Koul*

More articles in Statistics & Probability Letters from Elsevier

Bibliographic data for series maintained by Dana Niculescu ().