Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/116567


Title: Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data
Authors: 吳柏林
Lin, Pei-Chun
Wu, Berlin
Watada, Junzo
Contributors: 應數系
Keywords: Weight Function; Fuzzy Numbe; Appendix Table ;Triangular Fuzzy Number ;Empirical Distribution Function 
Date: 2010
Issue Date: 2018-03-27 15:57:10 (UTC+8)
Abstract: 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.
Relation: Advances in Soft Computing, Springer Verlag, pp.175-186
Integrated Uncertainty Management and Applications pp 175-186
Data Type: book/chapter
DOI 連結: https://doi.org/10.1007/978-3-642-11960-6_17
Appears in Collections:[應用數學系] 專書/專書篇章

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