Week5Student2009

Week5Student2009 - Week 5 Lecture 9 A lower bound by...

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Unformatted text preview: Week 5 Lecture 9 A lower bound by Tsybakov Parameter space = { , 1 ,..., M } (1) d ( i , j ) 2 s , for all 0 i 6 = j M. Usually s is the rate of convergence you have obtained by a specific procedure, and d is a distance related to the loss function. Reduction to bounds in probability For any which may not be in , define * = arg min j d , j Then inf sup E d 2 , s 2 inf sup P d , s s 2 inf sup j M P j * 6 = j = s 2 inf sup j M P j 6 = j Usually we construct the parameter in a way such that the minimax probability of error p e,M = inf sup j M P j 6 = j c for some fixed constant c > 0, then a lower bound cs 2 is obtained. Lower bound for minimax probability of error p e,M sup > M 1 + M (2) where = 1 M M j =1 P j ( A j ) with A j = n d P d P j > o ....
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Week5Student2009 - Week 5 Lecture 9 A lower bound by...

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