### Abstract

This paper aims to statistically test the null hypothesis H_{0} 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 H_{0} is true. The p_{value} 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 H_{0} 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.

Original language | English |
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Pages (from-to) | 63-75 |

Number of pages | 13 |

Journal | International Journal of Computational Intelligence Systems |

Volume | 8 |

DOIs | |

Publication status | Published - Dec 11 2015 |

### Fingerprint

### Keywords

- fuzzy samples
- percentile Bootstrap procedure
- resemblance of fuzzy samples
- simulation-based algorithm

### ASJC Scopus subject areas

- Computational Mathematics
- Computer Science(all)

### Cite this

*International Journal of Computational Intelligence Systems*,

*8*, 63-75. https://doi.org/10.1080/18756891.2015.1129592

**Bootstrap Kuiper Testing of the Identity of 1D Continuous Distributions using Fuzzy Samples.** / Nikolova, Natalia; Chai, Shuhong; Ivanova, Snejana D.; Kolev, K.; Tenekedjiev, Kiril.

Research output: Contribution to journal › Article

*International Journal of Computational Intelligence Systems*, vol. 8, pp. 63-75. https://doi.org/10.1080/18756891.2015.1129592

}

TY - JOUR

T1 - Bootstrap Kuiper Testing of the Identity of 1D Continuous Distributions using Fuzzy Samples

AU - Nikolova, Natalia

AU - Chai, Shuhong

AU - Ivanova, Snejana D.

AU - Kolev, K.

AU - Tenekedjiev, Kiril

PY - 2015/12/11

Y1 - 2015/12/11

N2 - 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.

AB - 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.

KW - fuzzy samples

KW - percentile Bootstrap procedure

KW - resemblance of fuzzy samples

KW - simulation-based algorithm

UR - http://www.scopus.com/inward/record.url?scp=84950108462&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84950108462&partnerID=8YFLogxK

U2 - 10.1080/18756891.2015.1129592

DO - 10.1080/18756891.2015.1129592

M3 - Article

AN - SCOPUS:84950108462

VL - 8

SP - 63

EP - 75

JO - International Journal of Computational Intelligence Systems

JF - International Journal of Computational Intelligence Systems

SN - 1875-6891

ER -