The Experts below are selected from a list of 57198 Experts worldwide ranked by ideXlab platform
Cyrille Joutard - One of the best experts on this subject based on the ideXlab platform.
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Asymptotic approximation for the probability density function of an arbitrary sequence of random variables
Statistics and Probability Letters, 2014Co-Authors: Cyrille JoutardAbstract:We establish a large deviation approximation for the density function of an arbitrary sequence of random variables. The results are analogous to those obtained by Chaganty and Sethuraman (1985). We apply our theorems to the Sample variance and the Mann–Whitney two-Sample Statistic.
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Strong large deviations for arbitrary sequences of random variables
Annals of the Institute of Statistical Mathematics, 2013Co-Authors: Cyrille JoutardAbstract:We establish a large deviation approximation for the density function of an arbitrary sequence of random variables. The results are analogous to those obtained by Chaganty and Sethuraman (1985). We apply our theorems to the Sample variance and the Mann–Whitney two-Sample Statistic.
Manuel Úbeda-flores - One of the best experts on this subject based on the ideXlab platform.
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A multivariate version of Gini's rank association coefficient
Statistical Papers, 2007Co-Authors: Javad Behboodian, Ali Dolati, Manuel Úbeda-floresAbstract:In this paper, we introduce a multivariate generalization of the population version of Gini's rank association coefficient, giving a response to this open question posed in [4]. We also study some properties of this version, present the corresponding results for the Sample Statistic, and provide several examples.
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Multivariate versions of Blomqvist's beta and Spearman's footrule
Annals of the Institute of Statistical Mathematics, 2005Co-Authors: Manuel Úbeda-floresAbstract:In this paper we define multivariate versions of the medial correlation coefficient and the rank correlation coefficient Spearman’s footrule in terms of copulas. We also present corresponding results for the Sample Statistic and provide a comparison of lower bounds among different measures of multivariate association.
Brunnere. - One of the best experts on this subject based on the ideXlab platform.
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Analysis of high-dimensional repeated measures designs
Computational Statistics & Data Analysis, 2008Co-Authors: Rauf Ahmadm., Wernerc., Brunnere.Abstract:A one Sample Statistic is derived for the analysis of repeated measures design when the data are multivariate normal and the dimension, d, can be large compared to the Sample size, n, i.e. d>n. Qua...
Markus Neuhauser - One of the best experts on this subject based on the ideXlab platform.
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advice on testing the null hypothesis that a Sample is drawn from a normal distribution
Animal Behaviour, 2015Co-Authors: Graeme D Ruxton, David M Wilkinson, Markus NeuhauserAbstract:The normal distribution remains the most widely used Statistical model, so it is only natural that researchers will frequently be required to consider whether a Sample of data appears to have been drawn from a normal distribution. Commonly used Statistical packages offer a range of alternative formal Statistical tests of the null hypothesis of normality, with inference being drawn on the basis of a calculated P value. Here we review the Statistical literature on the performance of these tests and briefly survey current usage of them in recently published papers, with a view to offering advice on good practice. We find that authors in Animal Behaviour seem to be using such testing most commonly in situations in which it is inadvisable (or at best unnecessary) involving pretesting to select parametric or nonparametric analyses, and making little use of it in model-fitting situations in which it might be of value. Of the many alternative tests, we recommend the routine use of either the Shapiro–Wilk or Chen–Shapiro tests; these are almost always superior to commonly used alternatives such as the Kolmogorov–Smirnov test, often by a substantial margin. We describe how both our recommended tests can be implemented. In contrast to current practice as indicated by our survey, we recommend that the results of these tests are reported in more detail (providing both the calculated Sample Statistic and the associated P value). Finally, we emphasize that even the higher-performing tests of normality have low power (generally below 0.5 and often much lower) when Sample sizes are less than 50, as is often the case in our field.
Waleed A. Yousef - One of the best experts on this subject based on the ideXlab platform.
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AUC: Nonparametric Estimators and Their Smoothness.
arXiv: Machine Learning, 2019Co-Authors: Waleed A. YousefAbstract:Nonparametric estimation of a Statistic, in general, and of the error rate of a classification rule, in particular, from just one available dataset through resampling is well mathematically founded in the literature using several versions of bootstrap and influence function. This article first provides a concise review of this literature to establish the theoretical framework that we use to construct, in a single coherent framework, nonparametric estimators of the AUC (a two-Sample Statistic) other than the error rate (a one-Sample Statistic). In addition, the smoothness of some of these estimators is well investigated and explained. Our experiments show that the behavior of the designed AUC estimators confirms the findings of the literature for the behavior of error rate estimators in many aspects including: the weak correlation between the bootstrap-based estimators and the true conditional AUC; and the comparable accuracy of the different versions of the bootstrap estimators in terms of the RMS with little superiority of the .632+ bootstrap estimator.
