Consider a regression through the origin, yi = Bx; +€j, V) =o²x}, i=1,2, ., 12 with E(e;) =0, a. Derive the generalized least squares estmate of ß in Eq. (4.58) and obtain its variance. Note that the covariance matrix V and its inverse V' are diagonal matrices. The generalized least squares estimate minimizes a weighted sum of squares with weights given by the diagonal elements in V-. Hence, one refers to it as the weighted least squares estimate. b. Suppose that z, = y,/x¡ and E" , 2 = 30. Find the numerical value for the weighted least squares estimate in (a) and express its variance as a function of o².
Consider a regression through the origin, yi = Bx; +€j, V) =o²x}, i=1,2, ., 12 with E(e;) =0, a. Derive the generalized least squares estmate of ß in Eq. (4.58) and obtain its variance. Note that the covariance matrix V and its inverse V' are diagonal matrices. The generalized least squares estimate minimizes a weighted sum of squares with weights given by the diagonal elements in V-. Hence, one refers to it as the weighted least squares estimate. b. Suppose that z, = y,/x¡ and E" , 2 = 30. Find the numerical value for the weighted least squares estimate in (a) and express its variance as a function of o².
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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
Transcribed Image Text:4.19. Consider a regression through the origin,
Yi = Bx; +€;, with E(e;)=0,
V(€) =o²x}, i=1,2,., 12
a. Derive the generalized least squares
estmate of ß in Eq. (4.58) and obtain its
variance. Note that the covariance matrix
V and its inverse V' are diagonal
matrices. The generalized least squares
estimate minimizes a weighted sum of
squares with weights given by the diagonal
elements in V-. Hence, one refers to it as
the weighted least squares estimate.
b. Suppose that z, = y;/x; and ", Z = 30.
Find the numerical value for the weighted
least squares estimate in (a) and express its
variance as a function of o².
%3D
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