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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク De-sparsified lasso ： ウィキペディア英語版 De-sparsified lasso De-sparsified lasso contributes to construct confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in high-dimensional model. ==1 High-dimensional linear model== $Y\; =\; X\backslash beta^0\; +\; \backslash epsilon$ with $n\; \backslash times\; p$ design matrix $X\; =:\; (X\_p)$ ($n\; \backslash times\; p$ vectors $X\_j$), $\backslash epsilon\; \backslash sim\; N\_n(0,\; \backslash sigma^2\_\backslash epsilon\; I)$ independent of $X$ and unknown regression $p\; \backslash times\; 1$ vector $\backslash beta^0$. The usual method to find the parameter is by Lasso: $\backslash hat^n(\backslash lambda)\; =\; \backslash underset\; \backslash \; \backslash frac\; \backslash left\backslash |\; Y\; -\; X\; \backslash beta\; \backslash right\backslash |\; ^\; 2\; \_\; 2\; +\; \backslash lambda\; \backslash left\backslash |\; \backslash beta\; \backslash right\backslash |\; \_\; 1$ The de-sparsified lasso is a method modified from the Lasso estimator which fulfills the Karush-Kuhn-Tucker conditions is as follows: $\backslash hat^n(\backslash lambda,M)\; =\; \backslash hat^n(\backslash lambda)\; +\; \backslash frac\; M\; X^T(Y-\; X\; \backslash hat^n\; (\backslash lambda))$ where $M\; \backslash in\; R\; ^(p\backslash times\; p)$ is an arbitrary matrix. The matrix $M$ is generated using a surrogate inverse covariance matrix. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「De-sparsified lasso」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.