翻訳と辞書 Words near each other ・ Additions to Daniel ・ Additive ・ Additive Architecture ・ Additive category ・ Additive color ・ Additive function ・ Additive genetic effects ・ Additive group ・ Additive identity ・ Additive increase/multiplicative decrease ・ Additive inverse ・ Additive K-theory ・ Additive Manufacturing File Format ・ Additive map ・ Additive Markov chain ・ Additive model ・ Additive number theory ・ Additive polynomial ・ Additive rhythm and divisive rhythm ・ Additive Schwarz method ・ Additive smoothing ・ Additive State Decomposition ・ Additive synthesis ・ Additive white Gaussian noise ・ Additively indecomposable ordinal ・ Additives for cut flowers ・ Additron tube ・ ADDitude Magazine ・ Additur ・ Addizione Erculea Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Additive model ： ウィキペディア英語版 Additive model In statistics, an additive model (AM) is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981)〔Friedman, J.H. and Stuetzle, W. (1981). "Projection Pursuit Regression", ''Journal of the American Statistical Association'' 76:817–823. 〕 and is an essential part of the ACE algorithm. The ''AM'' uses a one-dimensional smoother to build a restricted class of nonparametric regression models. Because of this, it is less affected by the curse of dimensionality than e.g. a ''p''-dimensional smoother. Furthermore, the ''AM'' is more flexible than a standard linear model, while being more interpretable than a general regression surface at the cost of approximation errors. Problems with ''AM'' include model selection, overfitting, and multicollinearity. ==Description== Given a data set $\backslash \backslash \}\_^n$ of ''n'' statistical units, where $\backslash \backslash \}\_^n$ represent predictors and $y\_i$ is the outcome, the ''additive model'' takes the form : $E(\backslash ldots,\; x\_)\; =\; \backslash beta\_0+\backslash sum\_^p\; f\_j(x\_)$ or : $Y=\; \backslash beta\_0+\backslash sum\_^p\; f\_j(X\_)+\backslash varepsilon$ Where $E(\backslash epsilon)\; =\; 0$, $Var(\backslash epsilon)\; =\; \backslash sigma^2$ and $E(f\_j(X\_))\; =\; 0$. The functions $f\_j(x\_)$ are unknown smooth functions fit from the data. Fitting the ''AM'' (i.e. the functions $f\_j(x\_)$) can be done using the backfitting algorithm proposed by Andreas Buja, Trevor Hastie and Robert Tibshirani (1989).〔Buja, A., Hastie, T., and Tibshirani, R. (1989). "Linear Smoothers and Additive Models", ''The Annals of Statistics'' 17(2):453–555. 〕 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「Additive model」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.