Statistical Inference Based on Judgment Post-Stratifed Samples in Finite Population

• Activity Number: 502
• Type: Contributed
• Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Omer Ozturk*
• Companies: The Ohio State University
• Keywords: Post stratifed sample ; Finite sample correction ; Ranked set sampling ; Rao-Blackwallized Estimator ; Stratified Sample ; Judgment ranking

Abstract

This paper draws statistical inference for finite population mean based on judgment post stratified (JPS) samples. The JPS sample first selects a simple random sample and then stratifies the selected units into H judgment classes based on their relative positions (ranks) in a small set of size H. This leads to a sample with random sample sizes in judgment classes. Ranking process can be performed either using auxiliary variables or visual inspection to identify the ranks of the measured observations. The paper develops unbiased estimator and constructs confidence interval for population mean. Since judgment ranks are random variables, by conditioning on the measured observations we construct Rao-Blackwallized estimators for the population mean. The paper shows that Rao-Blackwallized estimators perform better than usual JPS estimators. The proposed estimators are applied to 2012 United States Department of Agriculture Census Data.