Titlebook: Advances in Complex Data Modeling and Computational Methods in Statistics; Anna Maria Paganoni,Piercesare Secchi Book 2015 Springer Intern

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Building Multiple Regression Models,e graphical model. The first model is used to infer conditional independence graphs. The latter model is applied to compute the relative importance or contribution of each predictor to the response variables. Recently, penalized likelihood approaches have also been proposed to estimate graph structu

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Building Multiple Regression Models, contribute, presenting a topic and generating a discussion. In this paper, we propose the BarCamp as an innovative way of producing and communicating statistical knowledge, and we describe the experiment held at Politecnico di Milano, entitled “Technology Foresight and Statistics for the Future”.

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Nonlinear Multiple Regression Models,d biochemical features of the nuclear DNA are used to investigate salient properties and determinants of change (mutations) in the human genome. The studies under review, all conducted by an interdisciplinary group of investigators at The Pennsylvania State University, required the use of a range of

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Nonlinear Multiple Regression Models,everal canonical generalizations of non-Euclidean means. More involved data descriptors, for instance principal components generalize into even more complicated concepts. (Semi)-intrinsic statistical analysis allows to study inference on descriptors that can be represented as elements of another non

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