1,772 research outputs found
Simultaneous Prediction of Actual and Average Values of Study Variable Using Stein-rule Estimators
The simultaneous prediction of average and actual values of study variable in a linear regression model is considered in this paper. Generally, either of the ordinary least squares estimator or Stein-rule estimators are employed for the construction of predictors for the simultaneous prediction. A linear combination of ordinary least squares and Stein-rule predictors are utilized in this paper to construct an improved predictors. Their efficiency properties are derived using the small disturbance asymptotic theory and dominance conditions for the superiority of predictors over each other are analyzed
Risk Performance Of Stein-Rule Estimators Over The Least Squares Estimators Of Regression Coefficients Under Quadratic Loss Structures
This paper presents a general loss function under quadratic loss structure and discusses the comparison of risk functions associated with the unbiased least squares and biased Stein-rule estimators of the coefficients in a linear regression model
Stein-Rule Estimation under an Extended Balanced Loss Function
This paper extends the balanced loss function to a more general set
up. The ordinary least squares and Stein-rule estimators are exposed to
this general loss function with quadratic loss structure in a linear regression
model. Their risks are derived when the disturbances in the linear regression
model are not necessarily normally distributed. The dominance of ordinary
least squares and Stein-rule estimators over each other and the effect of
departure from normality assumption of disturbances on the risk property
is studied
Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions
The paper considers the construction of estimators of regression coefficients in a linear regression model when some stochastic and biased apriori information is available. Such apriori information is framed as stochastic restrictions. The dominance conditions of the estimators are derived under the criterion of mean squared error matrix
On the Estimation of the Linear Relation when the Error Variances are known
The present article considers the problem of consistent estimation in measurement error models. A linear relation with not necessarily normally distributed measurement errors is considered. Three possible estimators which are constructed as different combinations of the estimators arising from direct and inverse regression are considered. The efficiency properties of these three estimators are derived and analyzed. The effect of non-normally distributed measurement errors is analyzed. A Monte-Carlo experiment is conducted to study the performance of these estimators in finite samples and the effect of a non-normal distribution of the measurement errors
Performance of Double k-class Estimators for Coefficients in Linear Regression Models with Non Spherical Disturbances under Asymmetric Losses
The risk of the family of feasible generalized double k-class estimators under LINEX loss function is derived in a linear regression model. The disturbances are assumed to be non-spherical and their variance covariance matrix is unknown
- âŠ