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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 Least trimmed squares ： ウィキペディア英語版 Least trimmed squares Least trimmed squares (LTS), or least trimmed sum of squares, is a robust statistical method that fits a function to a set of data whilst not being unduly affected by the presence of outliers. It is one of a number of methods for robust regression. == Description of method == Instead of the standard least squares method, which minimises the sum of squared residuals over ''n'' points, the LTS method attempts to minimise the sum of squared residuals over a subset, ''k'', of those points. The ''n-k'' points which are not used do not influence the fit. In a standard least squares problem, the estimated parameter values, β, are defined to be those values that minimise the objective function, ''S''(β), of squared residuals :$S=\backslash sum\_^^2$, where the residuals are defined as the differences between the values of the dependent variables (observations) and the model values :$r\_i(\backslash beta)=\; y\_i\; -\; f(x\_i,\; \backslash beta),$ and where ''n'' is the overall number of data points. For a least trimmed squares analysis, this objective function is replaced by one constructed in the following way. For a fixed value of β, let $r\_(\backslash beta)$ denote the set of ordered absolute values of the residuals (in increasing order of absolute value). In this notation, the standard sum of squares function is :$S(\backslash beta)=\backslash sum\_^n\; (r\_(\backslash beta))^2,$ while the objective function for LTS is :$S\_k(\backslash beta)=\backslash sum\_^k\; (r\_(\backslash beta))^2.$ 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「Least trimmed squares」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.