lecture9

# lecture9 - ISYE6414 Summer 2010 Lecture 9 Shrinkage Methods...

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ISYE6414 Summer 2010 Lecture 9 Shrinkage Methods Dr. Kobi Abayomi July 6, 2010 1 Orthogonalization The best scenario for the observed data x in multiple regression is each x j x k : the observed data are linearly independent. Remember a linear regression is a conditional expectation of the response variable Y given the observed data X = x — if the covariate predictors are completely linearly independent, they form an orthogonal basis for Y . Perfect Collinearity is the opposite of linear orthogonality: the predictors x form a degenerate (deficient rank) basis for Y . The regression coefficient estimates ˆ β are non-identifiable, in this stiuation, and their variance is inflated. The following methods are designed to mitigate the effects of collinearity (linear dependence) between the predictors by replacing them with components — linear combinations — that are generated to be linearly independent. 2 Principal Components Regression (PCR) Recall that x T x = ˆ Σ is the estimate of the covariate matrix of the predictors. Call λ = ( λ 1 , ..., λ k ) the eigenvalues of ˆ Σ, and e its eigenvectors . As such ˆ Σ = e Λ e T , with Λ = diag ( λ 1 , ..., λ k ). The jth principal component of x is z j = e T j x = e j 1 x 1 + · · · + e jk x k 1

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Constructing the principal components as the inner products of the eigenvectors and the predictors yields principal components z 1 , ..., z k with these properties V ar ( z j ) = λ i Cov ( z j , z k ) = 0 from the linear orthogonality of the the eigenvectors. This is the Principal Component Anal- ysis (PCA) procedure. Principal Component Regression (PCR) is replacing the predictors in the regression equation with their linearly orthogonal linear combinations: replace ˆ y = x T ˆ β with ˆ y = z T ˆ β * . The goal in the PCR program is to remove linear dependence and to express ˆ y via a low number of components. Here’s an example in R library(faraway) data(meatspec) ####data on fat content of 215 samples of meat ###with 100 channel spectrum of absorbances ###predict fat content from spectrum data ###variables 1-100 range of spectrum ###training sample is first 172 observations model1 <- lm(fat ~ ., meatspec[1:172,]) summary(model1)\$r.squared ###use RMSE as a stat for gof for training and test sample rmse <- function(x,y) sqrt(mean((x-y)^2)) rmse(model1\$fit,meatspec\$fat[1:172])
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