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題名 Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data
作者 吳柏林
Lin, Pei-Chun
Wu, Berlin
Watada, Junzo
貢獻者 應數系
關鍵詞 Weight Function ;  Fuzzy Numbe ;  Appendix Table  ; Triangular Fuzzy Number  ; Empirical Distribution Function 
日期 2010
上傳時間 27-Mar-2018 15:57:10 (UTC+8)
摘要 The Kolmogorov-Smirnov two-sample test (K-S two sample test) is a goodness-of-fit test which is used to determine whether two underlying one-dimensional probability distributions differ. In order to find the statistic pivot of a K-S two-sample test, we calculate the cumulative function by means of empirical distribution function. When we deal with fuzzy data, it is essential to know how to find the empirical distribution function for continuous fuzzy data. In our paper, we define a new function, the weight function that can be used to deal with continuous fuzzy data. Moreover we can divide samples into different classes. The cumulative function can be calculated with those divided data. The paper explains that the K-S two sample test for continuous fuzzy data can make it possible to judge whether two independent samples of continuous fuzzy data come from the same population. The results show that it is realistic and reasonable in social science research to use the K-S two-sample test for continuous fuzzy data.
關聯 Advances in Soft Computing, Springer Verlag, pp.175-186
Integrated Uncertainty Management and Applications pp 175-186
資料類型 book/chapter
DOI https://doi.org/10.1007/978-3-642-11960-6_17
dc.contributor 應數系zh_Tw
dc.creator (作者) 吳柏林zh_TW
dc.creator (作者) Lin, Pei-Chunen_US
dc.creator (作者) Wu, Berlinen_US
dc.creator (作者) Watada, Junzoen_US
dc.date (日期) 2010-
dc.date.accessioned 27-Mar-2018 15:57:10 (UTC+8)-
dc.date.available 27-Mar-2018 15:57:10 (UTC+8)-
dc.date.issued (上傳時間) 27-Mar-2018 15:57:10 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/116567-
dc.description.abstract (摘要) The Kolmogorov-Smirnov two-sample test (K-S two sample test) is a goodness-of-fit test which is used to determine whether two underlying one-dimensional probability distributions differ. In order to find the statistic pivot of a K-S two-sample test, we calculate the cumulative function by means of empirical distribution function. When we deal with fuzzy data, it is essential to know how to find the empirical distribution function for continuous fuzzy data. In our paper, we define a new function, the weight function that can be used to deal with continuous fuzzy data. Moreover we can divide samples into different classes. The cumulative function can be calculated with those divided data. The paper explains that the K-S two sample test for continuous fuzzy data can make it possible to judge whether two independent samples of continuous fuzzy data come from the same population. The results show that it is realistic and reasonable in social science research to use the K-S two-sample test for continuous fuzzy data.en_US
dc.format.extent 219671 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Advances in Soft Computing, Springer Verlag, pp.175-186-
dc.relation (關聯) Integrated Uncertainty Management and Applications pp 175-186-
dc.subject (關鍵詞) Weight Function ;  Fuzzy Numbe ;  Appendix Table  ; Triangular Fuzzy Number  ; Empirical Distribution Function en_US
dc.title (題名) Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Dataen_US
dc.type (資料類型) book/chapter-
dc.identifier.doi (DOI) 10.1007/978-3-642-11960-6_17-
dc.doi.uri (DOI) https://doi.org/10.1007/978-3-642-11960-6_17-