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2016,Statistical Analysis for High-Dimensional Data pdf

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  • TA的每日心情
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    2016-3-19 06:18
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    [LV.4]偶尔看看III

    发表于 2016-3-21 03:43:52 | 显示全部楼层 |阅读模式
    Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014 (Abel Symposia)Mar 19, 2016
    by Arnoldo Frigessi and Peter Bühlmann
    41P5eulUljL._SX329_BO1,204,203,200_.jpg

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    This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014.

    The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection.

    Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

    From the Back Cover
    This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014.

    The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection.

    Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

    About the Author
    Marina Vannucci is a Professor of Statistics at Rice University. Her research focuses on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and their applications. Her work is often motivated by real problems that need to be addressed with suitable statistical methods.

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  • TA的每日心情
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    2017-12-15 11:07
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    [LV.7]常住居民III

    发表于 2016-6-30 14:49:27 | 显示全部楼层
    谢谢楼主辛苦分享!!!!
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    发表于 2016-12-2 10:36:31 | 显示全部楼层
    Thanks a lot.

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  • TA的每日心情
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    2020-6-15 21:59
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    [LV.9]以坛为家II

    发表于 2017-2-10 04:57:21 来自手机 | 显示全部楼层
    Thanks a lot.
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    2017-8-21 10:57
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    [LV.1]初来乍到

    发表于 2017-7-25 19:00:38 | 显示全部楼层
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    发表于 2017-10-4 21:38:40 | 显示全部楼层
    Game Theory Through Examples
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    2017-12-26 17:24
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    [LV.1]初来乍到

    发表于 2017-12-25 20:49:22 | 显示全部楼层
    感谢大神作者,祝你圣诞快乐!我也好好学习
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    发表于 2018-9-15 16:06:52 | 显示全部楼层
    亟待提高数学基础,谢谢分享
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