头盔
发表于 2025-3-28 17:10:40
A Study of Lack-of-Fit Diagnostics for Models Fit to Cross-Classified Binary Variables,. The extended . statistic is obtained by decomposing the Pearson statistic from the full table into orthogonal components defined on marginal distributions. The extended version of the statistic, ., can be applied to a variety of models for cross-classified tables. Simulation results show that . ha
污秽
发表于 2025-3-28 21:26:15
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convulsion
发表于 2025-3-29 00:18:39
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MORPH
发表于 2025-3-29 06:50:01
Parsimonious Mixtures of Matrix-Variate Shifted Exponential Normal Distributions,lly high number of parameters to be estimated. Thus, in this work we introduce a family of 196 parsimonious mixture models based on the matrix-variate shifted exponential normal distribution, an elliptical heavy-tailed generalization of the matrix-variate normal distribution. Parsimony is introduced
浮雕宝石
发表于 2025-3-29 10:58:58
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Ptosis
发表于 2025-3-29 14:00:52
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多山
发表于 2025-3-29 16:15:11
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Proclaim
发表于 2025-3-29 20:21:13
1431-8814 ing, and cyber risk.Fosters new directions of research in cl.This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clusterin
解脱
发表于 2025-3-30 00:43:24
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Blood-Vessels
发表于 2025-3-30 05:58:52
Parsimonious Mixtures of Matrix-Variate Shifted Exponential Normal Distributions,nious matrix-variate normal mixtures, thus allowing for a better modeling of datasets having atypical observations. Parameter estimation is obtained by using an ECM algorithm. The proposed models are then fitted to a real dataset along with parsimonious matrix-variate normal mixtures for comparison purposes.