Since the impressive works of Talagrand, concentration inequalities have been recognized as fundamental tools in several domains such as geometry of Banach spaces or random combinatorics. They also turn out to be essential tools to develop a non-asymptotic theory in statistics, exactly as the central limit theorem and large deviations are known to play a central part in the asymptotic theory. An overview of a non-asymptotic theory for model selection is given here and some selected applications to variable selection, change points detection and statistical learning are discussed. This volume reflects the content of the course given by P. Massart in St. Flour in 2003. It is mostly self-contained and accessible to graduate students.
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看這書總讓我想起大鳥談及法國人民，在麵對能用排列組閤處理的問題時候，楞要倔強地刨根兒到集閤的交並上。人民群眾辛辛苦苦倒騰齣來的不等式，老大上來你媽就弄各種Duality, Isometry，然後屠滅啊！
评分這纔是以統計學習理論為目的入門Concentration Inequality和Model Selection的必備啊，比Bouerchon & Lugosi & Massart簡潔瞭不少，最主要是還帶瞭Model Selection，更劃算。
评分這纔是以統計學習理論為目的入門Concentration Inequality和Model Selection的必備啊，比Bouerchon & Lugosi & Massart簡潔瞭不少，最主要是還帶瞭Model Selection，更劃算。
评分這纔是以統計學習理論為目的入門Concentration Inequality和Model Selection的必備啊，比Bouerchon & Lugosi & Massart簡潔瞭不少，最主要是還帶瞭Model Selection，更劃算。
评分這纔是以統計學習理論為目的入門Concentration Inequality和Model Selection的必備啊，比Bouerchon & Lugosi & Massart簡潔瞭不少，最主要是還帶瞭Model Selection，更劃算。
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