翻訳と辞書 Words near each other ・ Generalized hypergeometric function ・ Generalized hyperhidrosis ・ Generalized integer gamma distribution ・ Generalized inverse ・ Generalized inverse Gaussian distribution ・ Generalized inversive congruential pseudorandom numbers ・ Generalized iterative scaling ・ Generalized Jacobian ・ Generalized Kac–Moody algebra ・ Generalized keyboard ・ Generalized Korteweg–de Vries equation ・ Generalized Lagrangian mean ・ Generalized least squares ・ Generalized lentiginosis ・ Generalized lifting ・ Generalized linear array model ・ Generalized linear mixed model ・ Generalized linear model ・ Generalized logistic distribution ・ Generalized Lotka–Volterra equation ・ Generalized lymphadenopathy ・ Generalized map ・ Generalized Maxwell model ・ Generalized mean ・ Generalized method of moments ・ Generalized minimal residual method ・ Generalized minimum-distance decoding ・ Generalized Multi-Protocol Label Switching ・ Generalized multidimensional scaling ・ Generalized multivariate log-gamma distribution 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 Generalized linear array model ： ウィキペディア英語版 Generalized linear array model In statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. It based on the generalized linear model with the design matrix written as a Kronecker product. == Overview == The generalized linear array model or GLAM was introduced in 2006. Such models provide a structure and a computational procedure for fitting generalized linear models or GLMs whose model matrix can be written as a Kronecker product and whose data can be written as an array. In a large GLM, the GLAM approach gives very substantial savings in both storage and computational time over the usual GLM algorithm. Suppose that the data $\backslash mathbf\; Y$ is arranged in a $d$-dimensional array with size $n\_1\backslash times\; n\_2\backslash times\backslash ldots\backslash times\; n\_d$; thus,the corresponding data vector $\backslash mathbf\; y\; =\; \backslash textbf(\backslash mathbf\; Y)$ has size $n\_1n\_2n\_3\backslash cdots\; n\_d$. Suppose also that the design matrix is of the form :$\backslash mathbf\; X\; =\; \backslash mathbf\; X\_d\backslash otimes\backslash mathbf\; X\_\backslash otimes\backslash ldots\backslash otimes\backslash mathbf\; X\_1.$ The standard analysis of a GLM with data vector $\backslash mathbf\; y$ and design matrix $\backslash mathbf\; X$ proceeds by repeated evaluation of the scoring algorithm :$\backslash mathbf\; X\text{'}\backslash tilde\_\backslash delta\backslash mathbf\; X\backslash hat\; =\; \backslash mathbf\; X\text{'}\backslash tilde\_\backslash delta\backslash tilde\; ,$ where $\backslash tilde$ represents the approximate solution of $\backslash boldsymbol\backslash theta$, and $\backslash hat$ is the improved value of it; $\backslash mathbf\; W\_\backslash delta$ is the diagonal weight matrix with elements :$w\_^\; =\; \backslash left(\backslash frac\backslash right)^2\backslash text(y\_i),$ and :$\backslash mathbf\; z\; =\; \backslash boldsymbol\backslash eta\; +\; \backslash mathbf\; W\_\backslash delta^(\backslash mathbf\; y\; -\; \backslash boldsymbol\backslash mu)$ is the working variable. Computationally, GLAM provides array algorithms to calculate the linear predictor, :$\backslash boldsymbol\backslash eta\; =\; \backslash mathbf\; X\; \backslash boldsymbol\backslash theta$ and the weighted inner product :$\backslash mathbf\; X\text{'}\backslash tilde\_\backslash delta\backslash mathbf\; X$ without evaluation of the model matrix $\backslash mathbf\; X\; .$ 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「Generalized linear array 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.