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chap7

# chap7 - CHAPTER 7 MISCELLANEOUS TOPICS IN REGRESSION 1...

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CHAPTER 7 MISCELLANEOUS TOPICS IN REGRESSION 1. Weighted and Generalized Least Squares 2. Testing and correcting for heteroscedastic- ity 3. Polynomial regression and response surface methodology 4. Nonlinear regression 1

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1. Weighted and Generalized Least Squares Consider the model y i = p X j =1 x ij β j + ² i , 1 i n, (1) where the { ² i } are uncorrelated with mean 0 but, in contrast to earlier chapters, do not have common variance. In many cases it is possi- ble (exactly or hypothetically) to determine the variances up to an unknown constant, and this suggests a model of the form Var( ² i ) = σ 2 v i , (2) where the { v i } are known and > 0. 2
The appropriate generalization of least squares is weighted least squares : choose the parame- ters b β 1 , ..., b β p to minimize n X i =1 v - 1 i y i - p X j =1 x ij b β j 2 . (3) It is intuitively clear that the weight on the i ’th observation should decrease as v i increases, but it is not instantly obvious why the weights should be proportional to v - 1 i . There are at least four justifications of (3). Three of them are as follows: 3

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1. Rescaling the model Define y * i = v - 1 / 2 i y i , x * ij = v - 1 / 2 i x ij , ² * i = v - 1 / 2 i ² i . Then equation (1) is identical to y * i = p X j =1 x * ij β j + ² * i , 1 i n, (4) in which the coefficients { β j } are unchanged, but we now have Var( ² * i ) = σ 2 all equal. The least squares criterion for (4) is to choose b β 1 , ..., b β p to minimize n X i =1 y * i - p X j =1 x * ij b β j 2 which is the same as (3). 4
2. Weighted least squares estimate are BLUE It’s true! (Proof left as an exercise.) 5

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3. Maximum likelihood When (2) holds, an extension of (5.12) and (5.13) shows that the likelihood function is given by L = n Y i =1 (2 πσ 2 v i ) - 1 / 2 exp ( - ( y i - j x ij β j ) 2 2 σ 2 v i ) . Maximizing this with respect to β 1 , ..., β p is equiv- alent to minimizing (3). 6
Grouped data A specific context where the “right” answer is clear-cut, but also an approximation to the general case. Suppose y i = k y 0 ik /N i where y 0 i 1 , ..., y 0 iN i are independent data points (with common vari- ance) sampled at the same ( x i 1 , ..., x ip ) vector. Then Var( y i ) = σ 2 /N i so the model (2) holds with v i = N - 1 i . The ordinary least squares criterion applied to the { y 0 ik } implies that we should choose b β 1 , ..., b β p to minimize X i X k y 0 ik - p X j =1 x ij b β j 2 . (5) However, by adding and subtracting y i inside the parentheses, we easily see that (5) is the same as X i X k ( y 0 ik - y i ) 2 + X i N i y i - p X j =1 x ij b β j 2 . (6) 7

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The first term is independent of the unknown parameters, so minimizing (6) is equivalent to minimizing (3) with v i = N - 1 i . This provides our fourth method of justify- ing (3). Whenever the { v i } are of the form v i = AN - 1 i for some constant A and integers N 1 , ..., N n , then the experiment is identical to a grouped data experiment and so directly jus- tifies (3). However, by a process of rational approximation, we can get arbitrarily close to this situation for any v 1 , ..., v n , so (3) is justified in general.
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