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This article considers a linear regression model with some missing observations on the response variable and presents two
estimators of regression coefficients employing the approach of minimum risk estimation. Small disturbance asymptotic properties
of these estimators along with the traditional unbiased estimator are analyzed and conditions, that are easy to check in practice,
for the superiority of one estimator over the other are derived.
Received May 2001 相似文献
2.
This paper considers an improved estimator of normal mean which is obtained by considering a feasible version of minimum
mean squared error estimator. The exact expression for the bias and the mean squared error are fairly complicated and do not
provide any guidelines as how to estimate the standard error of improved estimator. As is well known that any estimator without
a formula for standard error has little practical utility. We therefore derive unbiased estimators for the bias and mean squared
error of the improved estimator. Incidently, they turn out to be minimum variance unbiased estimators. Further, this exercise
yields a simple formula for estimating the standard error. Based on the criterion of estimated standard error, the efficiency
of the improved estimator with respect to the traditional unbiased estimator (i.e., sample mean) is examined numerically.
The relationship with asymptotic standard error is also studied. 相似文献
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