Estimating the distribution function using k-tuple ranked set samples

Document Type

Article

Publication Date

4-1-2008

Abstract

The basic assumption underlying the concept of ranked set sampling is that actual measurement of units is expensive, whereas ranking is cheap. This may not be true in reality in certain cases where ranking may be moderately expensive. In such situations, based on total cost considerations, k-tuple ranked set sampling is known to be a viable alternative, where one selects k units (instead of one) from each ranked set. In this article, we consider estimation of the distribution function based on k-tuple ranked set samples when the cost of selecting and ranking units is not ignorable. We investigate estimation both in the balanced and unbalanced data case. Properties of the estimation procedure in the presence of ranking error are also investigated. Results of simulation studies as well as an application to a real data set are presented to illustrate some of the theoretical findings. © 2007 Elsevier B.V. All rights reserved.

Identifier

37249008209 (Scopus)

Publication Title

Journal of Statistical Planning and Inference

External Full Text Location

https://doi.org/10.1016/j.jspi.2007.02.012

ISSN

03783758

First Page

929

Last Page

949

Issue

4

Volume

138

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