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Y(i) = u + e(i) i = 1,2,...N

such that e(i)s are statistically independent and u is a parameter

mean of e(i) = 0

and variance = [tex]\sigma(i)[/tex]^2

Find W(i) such that the linear estimator

[tex]\mu[/tex] = [tex]\sum[/tex]W(i)X(i) for i = 1 to N

has

mean value of [tex]\mu[/tex] = u

and E[[tex](u-\mu)^2[/tex] is a minimum

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# Weights of a linear estimator

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