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Unformatted text preview: CHAPTER 5 DIAGNOSTICS FOR MODEL SELECTION 1 5.1 Variable Selection P possible regressors, choose a subset of p ≤ P , p initially undetermined. Simplest solution: fit all 2 P possible models and compare their error sums of squares ( SSE s). By computing best model (smallest SSE ) for each p , the problem is reduced to choosing among P + 1 possible models, one for each p . This is known as “best subset regression”. Note, however, that even this idea may not be practical if P is too large. 2 Example using Nuclear Power Data p R 2 Variable names 1 0.46 PT 2 0.66 LS PT 3 0.76 D LS PT 4 0.81 D LS NE PT 5 0.83 D LS NE CT LN 6 0.86 D LS NE CT LN PT 7 0.86 D LS NE CT BW LN PT 8 0.87 D LT2 LS PR NE CT LN PT 9 0.87 D LT1 LT2 LS PR NE CT LN PT 10 0.87 D LT1 LT2 LS PR NE CT BW LN PT Best R 2 for each model order for nuclear power data. 3 The adjusted R 2 criterion The coefficient of multiple determination, usu ally denoted R 2 , is defined by R 2 = 1 SSE SSTO where SSE is the sum of squares of residuals and SSTO = ∑ ( y i ¯ y ) 2 is the total sum of squares. This measures the proportion of total sum of squares explained by the model. Adjusted R 2 defined by R 2 a = 1 ( n 1) SSE ( n p ) SSTO . (1) In practice, selects too large a p . 4 R 2 a R 2 p Variable names .826 .871 8 D LT2 LS PR NE CT LN PT .823 .857 6 D LS NE CT LN PT .821 .861 7 D LS NE CT BW LN PT .820 .861 7 D LS PR NE CT LN PT .820 .861 7 D LT2 LS NE CT LN PT .818 .871 9 D LT1 LT2 LS PR NE CT LN PT .818 .871 9 D LT2 LS PR NE CT BW LN PT .818 .865 8 D LS PR NE CT BW LN PT .815 .857 7 D LT1 LS NE CT LN PT .815 .863 8 D LT2 LS NE CT BW LN PT Nuclear power data: Best 10 models according to adjusted R 2 criterion. 5 Prediction error criteria Suppose y i is the i ’th data point and that x i is the corresponding vector of covariates. Then y i = x T i β + ² i . Suppose we are trying to predict the value of a future observation at the same covariate vector x i . This may be written in the form y * i = x T i β + ² * i , where ² * i is independent of ² i , with the same mean 0 and variance σ 2 . The obvious point predictor of y * i is ˆ y * i = x T i ˆ β, where ˆ β is the least squares estimator of β . If the assumed model is correct, then ˆ β is an un biased estimator of β , but in the present con text, we do not know the true model, so we do not assume that ˆ β is unbiased. 6 The mean squared prediction error (MSPE) is of the form E( ˆ y * i y * i ) 2 = E[ { x T i ( ˆ β β ) } 2 ] + σ 2 , (2) using the fact that ² * i is independent of all past observations and therefore of ˆ β . However, the + σ 2 term in (2) is present regardless of the model we adopt, so we concentrate on the first term on the right hand side, writing it as a decomposition into squared bias plus variance, and summing over all i to get a formula for the overall mean squared prediction error....
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 Fall '11
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 Regression Analysis, Yi, NE CT LN, CT LN PT

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