2015/04/28 by Armin Hatefi, Hatefi, Armin, Mohammad Jafari Jozani +1 · 3 citations
Decision Sciences · Mathematics · #62B10 #62D05 #62E15 #62F99 #62J99 #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #Artificial intelligence #Combinatorics #Computer science #Entropy (arrow of time) #FOS: Computer and information sciences #FOS: Mathematics #Fuzzy Systems and Optimization #Mathematics #Methodology (stat.ME) #Other Statistics (stat.OT) #Population #Rank (graph theory) #Ranking (information retrieval) #Sampling (signal processing) #Sampling design #Simple random sample #Statistics #Statistics Theory (math.ST) #Stratified sampling #Systematic sampling #math.ST #msc:62B10 #msc:62D05 #msc:62E15 #msc:62F99 #msc:62J99 #stat.ME #stat.OT #stat.TH
paper · pdf · doi:10.48550/arxiv.1504.07336
published in arXiv (Cornell University) (Cornell University) · 24 pages
arxiv created 2015/04/28 · openalex publication_date 2015/04/28 · arxiv updated 2015/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Partially rank-ordered set (PROS) sampling is a generalization of ranked set sampling in which rankers are not required to fully rank the sampling units in each set, hence having more flexibility to perform the necessary judgemental ranking process. The PROS sampling has a wide range of applications in different fields ranging from environmental and ecological studies to medical research and it has been shown to be superior over ranked set sampling and simple random sampling for estimating the population mean. In this paper, we study the Fisher information content and uncertainty structure of the PROS samples and compare them with those of simple random sample (SRS) and ranked set sample (RSS) counterparts of the same size from the underlying population. We study the uncertainty structure in terms of the Shannon entropy, Renyi entropy and Kullback-Leibler (KL) discrimination measures. Several examples including the FI of PROS samples from the location-scale family of distributions as well as a regression model are discussed.