# Estimating Heteroscedastic Variances in Linear Models

@article{Horn1975EstimatingHV, title={Estimating Heteroscedastic Variances in Linear Models}, author={Susan D. Horn and Roger A. Horn and David B. Duncan}, journal={Journal of the American Statistical Association}, year={1975}, volume={70}, pages={380-385} }

Abstract We describe an estimator of heteroscedastic variances in the Gauss-Markov linear model where E(e) = 0 and with σ i 2 and unknown. It may be thought of as an approximation to the MINQUE method which results in computational economy, positive estimates, and decreased mean square error. Properties of this almost unbiased estimator are stated. It is compared with other estimators, and extensions to more general models are discussed.

## 187 Citations

Comparison of Estimators of Heteroscedastic Variances in Linear Models

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Abstract Four methods for estimating heteroscedastic variances are discussed in this article: the MINQUE introduced by C.R. Rao [7], the AUE introduced by S.D. Horn, R.A. Horn, and D.B. Duncan [4],…

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Comparison of Estimators of Heteroscedastic Variances in Linear Models

- Mathematics
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Abstract Four methods for estimating heteroscedastic variances are discussed in this article: the MINQUE introduced by C.R. Rao [7], the AUE introduced by S.D. Horn, R.A. Horn, and D.B. Duncan [4],…

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Abstract Let Y=Xβ+e be a Gauss-Markoff linear model such that E(e) = 0 and D(e), the dispersion matrix of the error vector, is a diagonal matrix δ whose ith diagonal element is σi 2, the variance of…

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The method of minimum norm quadratic unbiased estimation (MINQUE) is applied to the estimation of the variances o-2 in the following problems: (1) Combining k independent estimators yi (i = 1, * , k)…

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