Asymptotically Optimal Tests for Heteroscedasticity in the General Linear Model
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چکیده
منابع مشابه
Tests of linear hypotheses in the ANOVA under heteroscedasticity
It is often of interest to undertake a general linear hypothesis testing (GLHT) problem in the one-way ANOVA without assuming the equality of the group variances. When the equality of the group variances is valid, it is well known that the GLHT problem can be solved by the classical F-test. The classical F test, however, may lead to misleading conclusions when the variance homogeneity assumptio...
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In the presence of heteroscedasticity, OLS estimates are unbiased, but the usual tests of significance are inconsistent. However, tests based on a heteroscedasticity consistent covariance matrix (HCCM) are consistent. While most applications using a HCCM appear to be based on the asymptotic version of the HCCM, there are three additional, relatively unknown, small sample versions of the HCCM th...
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15 صفحه اولStatistical Inference on the General Linear Models under Heteroscedasticity
Assuming a general linear model with unknown and possibly unequal normal error variances, the interest is to develop a one-sample procedure to handle the statistical problems involving point estimation, confidence region, and general linear hypotheses on regression parameters. The sampling procedure is to split up each single sample of size ni at a controllable regressor’s data point into two p...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1981
ISSN: 0090-5364
DOI: 10.1214/aos/1176345402