The moments of the operational almost unbiased ridge regression estimator

In this paper, we derive the exact general expressions for the moments of the Lawless-Wang's operational almost unbiased ridge regression (AUGRR) estimator for individual regression coefficients. © 2003 Elsevier Inc. All rights reserved

Generalized liu type estimators under Zellner's balanced loss function

In regression analysis, ridge regression estimators and Liu type estimators are often used to overcome the problem of multicollinearity. These estimators have been evaluated using the risk under quadratic loss criterion, which places sole emphasis on estimators' precision. The traditional mean square error (MSE) as the measure of efficiency of an estimator only takes the error of estimation into account. In 1994, Zellner proposed a balanced loss function. Here, we consider the balanced loss function which incorporates a measure for the goodness of fit of the model as well as estimation precision. We also examine the risk performance of the feasible generalized Liu estimator and feasible almost unbiased generalized Liu estimator when the balanced loss function is used. Copyright © Taylor & Francis, Inc.Çukurova ÜniversitesiThe authors are very grateful to the anonymous referee for valuable comments and suggestions. The first author was supported by the Çukurova University under grant No. FEF2004BAP1

The power of autocorrelation tests near the unit root in models with possibly mis-specified linear restrictions

It is well known that the Durbin–Watson and several other tests for first-order autocorrelation have limiting power of either zero or one in a linear regression model without an intercept, and a constant lying strictly between these values when an intercept term is present. This paper considers the limiting power of these tests in models with possibly incorrect restrictions on the coefficients. It is found that with linear restrictions on the coefficients, the limiting power can still drop to zero even with the inclusion of an intercept in the regression. Our results also accommodate the situation of a possibly mis-specified linear model

Further results on the generalized Liu-type estimators under the balanced loss function

In regression analysis, ridge regression estimators and Liu type estimators are often used to overcome the collinearity problem. These estimators have been evaluated using the risk under quadratic loss criterion, which places sole emphasis on estimators' precision. The traditional mean square error (MSE) as the measure of effciency of an estimator only takes the error of estimation into account. In 1994, Zellner proposed a balanced loss function. Recently, Akdeniz et al. [6] considered the balanced loss function which incorporates a measure for the goodness of fit of the model as well as the precision of estimation in the evaluation of the feasible generalized Liu estimator (FGLE) and almost unbiased feasible generalized Liu estimator (AUFGLE). In this paper, we derive, numerically evaluate and compare the risks of the FGLE and AUFGLE for four different degrees of multicollinearity under the balanced loss function. © 2007 IOS Press. All rights reserved

Least squares estimators in measurement error models under the balanced loss function

Balanced loss function, direct and reverse regression, ineasurement errors, ultrastructural model, 62J05,