Zobrazeno 1 - 10
of 46
pro vyhledávání: '"62G10, 62H15"'
In the statistical literature, as well as in artificial intelligence and machine learning, measures of discrepancy between two probability distributions are largely used to develop measures of goodness-of-fit. We concentrate on quadratic distances, w
Externí odkaz:
http://arxiv.org/abs/2407.16374
Autor:
Lukić, Žikica
In this paper, we present a novel test for determining equality in distribution of matrix distributions. Our approach is based on the integral squared difference of the empirical Laplace transforms with respect to the noncentral Wishart measure. We c
Externí odkaz:
http://arxiv.org/abs/2406.10733
In statistics and machine learning, measuring the similarity between two or more datasets is important for several purposes. The performance of a predictive model on novel datasets, referred to as generalizability, critically depends on how similar t
Externí odkaz:
http://arxiv.org/abs/2312.04078
Publikováno v:
Electron. J. Statist. 18 (2) 3021 - 3106, 2024
Statistical depth functions provide measures of the outlyingness, or centrality, of the elements of a space with respect to a distribution. It is a nonparametric concept applicable to spaces of any dimension, for instance, multivariate and functional
Externí odkaz:
http://arxiv.org/abs/2308.09869
Autor:
Huang, Zhen, Sen, Bodhisattva
The sign test (Arbuthnott, 1710) and the Wilcoxon signed-rank test (Wilcoxon, 1945) are among the first examples of a nonparametric test. These procedures -- based on signs, (absolute) ranks and signed-ranks -- yield distribution-free tests for symme
Externí odkaz:
http://arxiv.org/abs/2305.01839
Autor:
Huang, Zhen, Sen, Bodhisattva
Given $M \geq 2$ distributions defined on a general measurable space, we introduce a nonparametric (kernel) measure of multi-sample dissimilarity (KMD) -- a parameter that quantifies the difference between the $M$ distributions. The population KMD, w
Externí odkaz:
http://arxiv.org/abs/2210.00634
In order to adapt the Wasserstein distance to the large sample multivariate non-parametric two-sample problem, making its application computationally feasible, permutation tests based on the Sinkhorn divergence between probability vectors associated
Externí odkaz:
http://arxiv.org/abs/2209.14455
We consider the problem of detecting distributional changes in a sequence of high dimensional data. Our approach combines two separate statistics stemming from $L_p$ norms whose behavior is similar under $H_0$ but potentially different under $H_A$, l
Externí odkaz:
http://arxiv.org/abs/2207.08933
In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are
Externí odkaz:
http://arxiv.org/abs/2109.15261
We address the problem of testing for the invariance of a probability measure under the action of a group of linear transformations. We propose a procedure based on consideration of one-dimensional projections, justified using a variant of the Cram\'
Externí odkaz:
http://arxiv.org/abs/2109.01041