AbstractIn a subclass of elliptical distributions, Stein estimators are robust in estimating the mean vector and the regression parameters in a linear regression model. Unbiased estimates of bias and risk are also given for the regression model
AbstractThe problem of simultaneous estimation of the regression parameters in a multiple regression...
Stein’s result has transformed common belief in statistical world that the maximum likelihood estima...
AbstractThis paper considers a general family of Stein rule estimators for the coefficient vector of...
AbstractFor the family of multivariate normal distribution functions, Stein's Lemma presents a usefu...
For the family of multivariate normal distribution functions, Stein's Lemma presents a useful tool f...
253 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1985.Much work on the James-Stein ...
AbstractThis paper examines the role of Stein estimation in a linear ultrastructural form of the mea...
The paper investigates the effects of misspecifying the disturbances in a linear regression model as...
This paper generalizes Stein's Lemma recently obtained for elliptical class distributions to the gen...
This paper extends the balanced loss function to a more general set up. The ordinary least squares a...
This paper examines the role of Stein estimation in a linear ultrastructural form of the measurement...
Since 1956, a large number of papers have been devoted to Stein's technique of obtaining improved es...
In this paper, we consider a linear regression model when relevant regressors are omitted in the spe...
This paper derives the exact density of the Stein-rule estimator in the setting of the general linea...
In this note we furnish a set-up under which the Stein-rule estimator turns out to be a feasible ver...
AbstractThe problem of simultaneous estimation of the regression parameters in a multiple regression...
Stein’s result has transformed common belief in statistical world that the maximum likelihood estima...
AbstractThis paper considers a general family of Stein rule estimators for the coefficient vector of...
AbstractFor the family of multivariate normal distribution functions, Stein's Lemma presents a usefu...
For the family of multivariate normal distribution functions, Stein's Lemma presents a useful tool f...
253 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1985.Much work on the James-Stein ...
AbstractThis paper examines the role of Stein estimation in a linear ultrastructural form of the mea...
The paper investigates the effects of misspecifying the disturbances in a linear regression model as...
This paper generalizes Stein's Lemma recently obtained for elliptical class distributions to the gen...
This paper extends the balanced loss function to a more general set up. The ordinary least squares a...
This paper examines the role of Stein estimation in a linear ultrastructural form of the measurement...
Since 1956, a large number of papers have been devoted to Stein's technique of obtaining improved es...
In this paper, we consider a linear regression model when relevant regressors are omitted in the spe...
This paper derives the exact density of the Stein-rule estimator in the setting of the general linea...
In this note we furnish a set-up under which the Stein-rule estimator turns out to be a feasible ver...
AbstractThe problem of simultaneous estimation of the regression parameters in a multiple regression...
Stein’s result has transformed common belief in statistical world that the maximum likelihood estima...
AbstractThis paper considers a general family of Stein rule estimators for the coefficient vector of...
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