Shrinkage Estimation Using Ranked Set Samples

Hassen A. Muttlak
S. E. Ahmed
I. Rahimov

Document Type

Article

Source of Publication

Arabian Journal for Science and Engineering

Publication Date

10-1-2011

Abstract

The purpose of this article is two-fold. First, we consider the ranked set sampling (RSS) estimation and testing hypothesis for the parameter of interest (population mean). Then, we suggest some alternative estimation strategies for the mean parameter based on shrinkage and pretest principles. Generally speaking, the shrinkage and pretest methods use the non-sample information (NSI) regarding that parameter of interest. In practice, NSI is readily available in the form of a realistic conjecture based on the experimenter's knowledge and experience with the problem under consideration. It is advantageous to use NSI in the estimation process to construct improved estimation for the parameter of interest. In this contribution, the large sample properties of the suggested estimators will be assessed, both analytically and numerically. More importantly, a Monte Carlo simulation is conducted to investigate the relative performance of the estimators for moderate and large samples. For illustrative purposes, the proposed methodology is applied to a published data set. © 2011 King Fahd University of Petroleum and Minerals.

ISSN

1319-8025

Publisher

Springer Science and Business Media LLC

Volume

36

Issue

6

First Page

1125

Last Page

1138

Disciplines

Life Sciences

Keywords

Asymptotic properties, Errors in ranking, Local alternatives, Pretest and shrinkage estimation, Ranked set sampling, Relative precision, Replications

Scopus ID

80053311865

Indexed in Scopus

yes

Open Access

no

Link to Full Text

Share

COinS