Characterization of distributions symmetric with respect to a group of transformations and testing of corresponding statistical hypothesis

Klebanov, Lev B.; Kozubowski, Tomasz J.; Rachev, Svetlozar T.; Vol'kovich, V. É.

doi:10.1016/s0167-7152(01)00011-6

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…This test is based on a characterization theorem stated in Zinger, Kakosyan, and Klebanov (1989) . Applications of this approach have been discussed in Klebanov (2005) ; Klebanov, Kozubowskii, Rachev, and Volkovich (2001) . The two-sample energy test described in Zech and Aslan (2005) can also be interpreted in the framework of this methodology.…”

Section: Kolmogorov-smirnov Testmentioning

confidence: 99%

Literary writing style recognition via a minimal spanning tree-based approach

Shalymov

Granichin

Klebanov

et al. 2016

Expert Systems with Applications

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Section: Kolmogorov-smirnov Testmentioning

confidence: 99%

Literary writing style recognition via a minimal spanning tree-based approach

Shalymov

Granichin

Klebanov

et al. 2016

Expert Systems with Applications

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“…This test is based on a characterization theorem stated in Zinger et al (1989). Applications of this approach have been discussed in Klebanov et al (2001) (see also Klebanov 2005). The two-sample energy test described in Zech and Aslan (2005) can also be interpreted in the framework of this methodology.…”

Section: Simple Metricsmentioning

confidence: 99%

“…The idea of such a method is based on the characterization theorems stated in Zinger et al (1989) and Klebanov (2003). Several applications of this methodology are presented in Klebanov et al (2001) and Belopolskaya et al (2005) while a general clarification can be found in the book (Klebanov 2005).…”

Section: Negative Define Kernels Based Distancesmentioning

confidence: 99%

Resampling approach for cluster model selection

et al. 2011

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In cluster analysis, selecting the number of clusters is an "ill-posed" problem of crucial importance. In this paper we propose a re-sampling method for assessing cluster stability. Our model suggests that samples' occurrences in clusters can be considered as realizations of the same random variable in the case of the "true" number of clusters. Thus, similarity between different cluster solutions is measured by means of compound and simple probability metrics. Compound criteria result in validation rules employing the stability content of clusters. Simple probability metrics, in particular those based on kernels, provide more flexible geometrical criteria. We analyze several applications of probability metrics Editors: Süreyya Özögür-Akyüz, Devrim Ünay, and Alex Smola. Learn (2011) 85:209-248 combined with methods intended to simulate cluster occurrences. Numerical experiments are provided to demonstrate and compare the different metrics and simulation approaches.

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A cluster stability criteria based on the two-sample test concept

Volkovich

Barzily

Morozensky

Studies in Computational Intelligence

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Characterization of distributions symmetric with respect to a group of transformations and testing of corresponding statistical hypothesis

Cited by 7 publications

References 2 publications

Literary writing style recognition via a minimal spanning tree-based approach

Literary writing style recognition via a minimal spanning tree-based approach

Resampling approach for cluster model selection

A cluster stability criteria based on the two-sample test concept

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