Estimation of Population Mean and Variance in Animal Breeding Experiments: A Ranked Set Sampling Approach in a Finite Population Setting

• Activity Number: 239
• Type: Contributed
• Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics & the Environment
• Author(s): Omer Ozturk*+ and Omer Bilgin and Douglas A. Wolfe
• Companies: The Ohio State University, Marion and The Ataturk University and The Ohio State University
• Keywords: concomitant variable ; judgment ranking ; sampling design ; finite population correction ; stratified sampling ; unequal allocation

Abstract

Ranked set sampling is a sampling technique that provides substantial cost efficiency in experiments where a quick, inexpensive ranking procedure is available to rank the units prior to formal, expensive and precise measurements. Although the theoretical properties and relative efficiencies of this approach with respect to simple random sampling have been extensively studied in the literature for the infinite population setting, the use of ranked set sampling methods has not yet been explored widely for finite populations. The purpose of this study is to use an animal breeding data set from the Research Farm at Ataturk University, Erzurum, Turkey, to demonstrate the practical benefits of ranked set sampling procedures relative to the more commonly used simple random sampling estimation of the population mean and variance in a finite population. It is shown that the ranked set sample mean remains unbiased for the population mean, as is the case for the infinite population, but the variance estimators are unbiased only with use of the finite population correction factor. Both mean and variance estimators provide substantial improvement over their simple random sample analogs.