計畫編號:NSC89-2118-M163-001 研究期間:200008~200107 研究經費:229,000[[abstract]]For estimating a normal variance under squared error loss function it is well known that the best affine (location and scale) equivariant estimator, which is better than the maximum likelihood estimator as well as the unbiased estimator, is also inadmissible. The improved estimators, e.g., Stein type, Brown type and Brewster-Zidek type, are all scale equivariant but not location invariant. Lately a good amount of research has been done to compare the improved estimators in terms of risk, but very little attention had been paid to compare these estimators in terms of Pitman nearness criterion. In this paper we have undertaken a comprehensive study to compare various variance...
We give an equivalent definition of Pitman’s closeness criterion, in terms of medians of the differe...
Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, ro...
This article compares eight estimators in terms of relative efficiencies with the univariate mean, s...
[[abstract]]For estimating a normal variance under squared error loss function it is well known that...
[[abstract]]For estimating a normal variance under the squared error loss function it is well known ...
[[abstract]]For estimating a normal variance under the squared error loss function it is well known ...
The iterative Stein-rule estimator and the usual estimator of the error variance are compared under ...
For a large class of distributions and large samples, it is shown that estimates of the variance σ2 ...
A general method for determining Pitman Nearness is given In the case of univariate estimators. This...
Pitman's measure of closeness, closest estimator, Stein-type estimator, Brown-type estimator, equiva...
According to Pitman (1937), an estimator X is closer than an estimator Y to a scalar parameter [thet...
In a linear regression model with proxy variables, the iterative Stein-rule estimator and the usual ...
We consider component-wise estimation of order restricted location/scale parameters of a general biv...
In a multiparameter estimation problem, for first-order efficient estimators, second-order Pitman ad...
Measurement of dispersion and variation have been studied and evaluated in many applications. Volati...
We give an equivalent definition of Pitman’s closeness criterion, in terms of medians of the differe...
Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, ro...
This article compares eight estimators in terms of relative efficiencies with the univariate mean, s...
[[abstract]]For estimating a normal variance under squared error loss function it is well known that...
[[abstract]]For estimating a normal variance under the squared error loss function it is well known ...
[[abstract]]For estimating a normal variance under the squared error loss function it is well known ...
The iterative Stein-rule estimator and the usual estimator of the error variance are compared under ...
For a large class of distributions and large samples, it is shown that estimates of the variance σ2 ...
A general method for determining Pitman Nearness is given In the case of univariate estimators. This...
Pitman's measure of closeness, closest estimator, Stein-type estimator, Brown-type estimator, equiva...
According to Pitman (1937), an estimator X is closer than an estimator Y to a scalar parameter [thet...
In a linear regression model with proxy variables, the iterative Stein-rule estimator and the usual ...
We consider component-wise estimation of order restricted location/scale parameters of a general biv...
In a multiparameter estimation problem, for first-order efficient estimators, second-order Pitman ad...
Measurement of dispersion and variation have been studied and evaluated in many applications. Volati...
We give an equivalent definition of Pitman’s closeness criterion, in terms of medians of the differe...
Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, ro...
This article compares eight estimators in terms of relative efficiencies with the univariate mean, s...