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Bootstrap Kuiper testing of the identity of 1D continuous distributions using fuzzy samples

Citation

Nikolova, ND and Chai, S and Ivanova, SD and Kolev, K and Tenekedjiev, K, Bootstrap Kuiper testing of the identity of 1D continuous distributions using fuzzy samples, International Journal of Computational Intelligence Systems, 8, (Sup 2) pp. 63-75. ISSN 1875-6891 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 The Authors

DOI: doi:10.1080/18756891.2015.1129592

Abstract

This paper aims to statistically test the null hypothesis H0 for identity of the probability distribution of one-dimensional (1D) continuous parameters in two different populations, presented by fuzzy samples of i.i.d. observations. A degree of membership to the corresponding population is assigned to any of the observations in the fuzzy sample. The test statistic is the Kuiper's statistic, which measures the identity between the two sample cumulative distribution functions (CDF) of the parameter. A Bootstrap algorithm is developed for simulation-based approximation for the CDF of the Kuiper statistic, provided that H0 is true. The pvalue of the statistical test is derived using the constructed conditional distribution of the test statistic. The main idea of the proposed Bootstrap test is that, if H0 is true, then the two available fuzzy samples can be merged into a unified fuzzy sample. The latter is summarized into a conditional sample distribution of the 1D continuous parameter used for generation of synthetic pairs of fuzzy samples in different pseudo realities. The proposed algorithm has four modifications, which differ by the method to generate the synthetic fuzzy sample and by the type of the conditional sample distribution derived from the unified fuzzy sample used in the generation process. Initial numerical experiments are presented which tend to claim that the four modifications produce similar results.

Item Details

Item Type:Refereed Article
Keywords:fuzzy samples, percentile Bootstrap procedure, simulation-based algorithm, resemblance of fuzzy samples
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Applied Statistics
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
Author:Nikolova, ND (Professor Nataliya Nikolova)
Author:Chai, S (Associate Professor Shuhong Chai)
Author:Tenekedjiev, K (Professor Kiril Tenekedjiev)
ID Code:106430
Year Published:2015
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2016-02-10
Last Modified:2017-11-01
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