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Adapting for Heteroscedasticity in Linear Models
Raymond J. Carroll
The Annals of Statistics
Vol. 10, No. 4 (Dec., 1982), pp. 1224-1233
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2240725
Page Count: 10
You can always find the topics here!Topics: Linear regression, Statism, Linear models, Statistical discrepancies, Statistical estimation, Least squares, Cost estimates, Estimators, Perceptron convergence procedure, Parametric models
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In a heteroscedastic linear model, it is known that if the variances are a parametric function of the design, then one can construct an estimate of the regression parameter which is asymptotically equivalent to the weighted least squares estimate with known variances. We show that the same is true when the only thing known about the variances is that they are determined by an unknown but smooth function of the design or the mean response.
The Annals of Statistics © 1982 Institute of Mathematical Statistics