Estimating Heteroscedastic Variances in Linear Models - A Simpler Approach.
JOHNS HOPKINS UNIV BALTIMORE MD DEPT OF MATHEMATICAL SCIENCES
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The authors describe an estimator of heteroscedastic variances in the Gauss-Markov linear model 7 X beta epsilon where Eepsilon O and Var epsilon diagSigma sub 1, Sup 2,..., Sigma sub n, sup 2 with Sigma sub i, sup 2 and beta unknown. It may be thought of as an approximation to the MINQUE method, but it results in both computational economy and decreased mean square error. Properties of this approximately unbiased estimator are stated and it is compared with other estimators. Extensions to more general models are discussed. Author
- Statistics and Probability