BEST-Linear-Estimators - Best Linear Estimators ARE 210...

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Best Linear Estimators ARE 210 Page 1 BLE, BLUE and BLMSE 1. How do we estimate the unknown parameters of a probability distribution? 2. What kind of inferences can we make based on those parameter estimates? 3. Under what conditions is our rule for estimating these unknown parameters optimal in some reasonable sense? 4. When can we do better, and when not? In these notes, I develop three kinds of estimators that are linear in the observations (data) that can all be thought of in terms of optimization theory. The BLE (the B est L inear E stimator) chooses a linear combination of the observations from a random sample to minimize the variance without constraint. The result is weight zero on each and every data point. While this estimator does in fact attain the global un- restricted minimum of the variance for an estimator (its variance is always zero!), it may be biased. Indeed, it will be biased with probability one for all sample sizes and probabil- ity distributions. The BLUE (B est L inear U nbiased E stimator) principle minimizes the variance of the chosen linear combination of the data subject to the constraint that the estimator must be unbiased. The BLMSE (B est L inear M ean S quared E rror E stimator) principle weights the square of the bias equally with the variance in the objective function and minimizes the unre- stricted global minimum of the sum (bias 2 + variance).
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Best Linear Estimators ARE 210 Page 2 We begin by supposing that we have a random sample of i.i.d. random variables, 12 ,,, n yy y " . Let the population mean for the underlying probability distribution for the y ’s be µ and let the population variance be σ 2 . Both of these are unknown. For now, we will not make any further assumptions about the distribution. We do not as- sume that we know the functional form of the pdf (such as normal). We will, however, restrict our attention to linear combinations of the data, say 1 ˆ n ii i wy = µ= , where the “weights” w i are choice variables, to make calculating expectations simpler and to pose the estimation problem better. Writing the mean of ˆ µ as () ( ) 11 1 ˆ ( ) nn n i i i i EE w y w E y w == = = = µ ∑∑ , (1) the variance of ˆ µ is equal to [] {} ( ) 2 2 2 ˆ ˆˆ i E w y w µ σ= µ− µ = µ  { } 2 1 n i Ew y = =− µ . (2) We seek to choose the weights w i , for i = 1,…, n , to minimize this function. Using the composite function theorem, the necessary first-order conditions are 2 ˆ 1 2( ) ( ) 0 1, , n ij j j i Ey w y i n w µ = ∂σ  µ µ = =  " . (3)
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This note was uploaded on 08/01/2008 for the course ARE 210 taught by Professor Lafrance during the Fall '07 term at University of California, Berkeley.

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BEST-Linear-Estimators - Best Linear Estimators ARE 210...

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