Characterizations via regression of generalized order statistics |
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| Affiliation: | 1. Department of Mathematics and Statistics, University of Hyderabad, Hyderabad 500046, India;2. Department of Management Sciences, Rider University, Lawrenceville, NJ 08648-3099, USA;3. Department of Mathematics and Statistics, University of Maine, Orono, ME 04469, USA;1. School of Automotive Studies, Tongji University, Shanghai 201804, PR China;2. Institute for Energy Conservation, Jiaxing University, Jiaxing 314001, PR China;3. Shanghai Sunwise Energy Systems Co., Ltd., Shanghai 201805, PR China;1. U.S Department of Agriculture, Agricultural Research Service, Roman L. Hruska U.S. Meat Animal Research Center, Clay Center, NE, USA;2. Department of Veterinary Pathobiology, Texas A&M University, College Station, TX, USA;1. Faculty of Engineering, Niigata University, 8050, Ikarashi 2, Nishi, Niigata 950-2181, Japan;2. Faculty of Engineering, Shinshu University, 4-17-1 Wakasato, Nagano 380-8553, Japan;3. Graduate School of Science and Technology, Niigata University, 8050, Ikarashi 2, Nishi, Niigata 950-2181, Japan;1. School of Geography and Tourism, Guangdong University of Finance & Economics, China;2. Institute of Transport Geography and Spatial Planning, Shaanxi Normal University, China;3. School of Geography and Planning, Sun Yat-Sen University, China;4. School of economics and Trade, Guangdong University of Finance & Economics, China;1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China;2. Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China |
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| Abstract: | In this paper, we present some characterizations of distributions based on the regression of generalized order statistics. In the case of adjacent generalized order statistics, the conditional expectation of one generalized order statistic given the other one completely characterizes distributions depending on the type of regression function. In the case of non-adjacent generalized order statistics, the characterization of distributions using conditional expectations becomes more complicated. The results presented in the paper unify and extend some of the existing results involving order statistics and record values. |
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