Best Linear Unbiased and Invariant Estimators for Regression Parameters Based on Ordered Observations.
KENTUCKY UNIV LEXINGTON DEPT OF STATISTICS
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The best linear unbiased estimators of the parameters in the multiple regression model are obtained when the samples are arbitrarily censored. The homogeneity of variance, polynomial regression and simple linear regression become special cases of the above model. The formulas take simpler forms when the underlying distribution is symmetric and the subsamples are of equal size and are symmetrically censored. Best constant-risk estimators of the parameters alpha, beta, and sigma in the simple linear regression model are obtained. These results are applied to symmetric uniform and negative exponential cases. Author
- Statistics and Probability