When independent variables have high linear correlation in a multiple linear regression model, we can have wrong analysis. It happens if we do the multiple linear regression analysis based on common Ordinary Least Squares (OLS) method. In this situation, we are suggested to use ridge regression estimator. We conduct some simulation study to compare the performance of ridge regression estimator ...

The present article deals with the problem of estimation of parameters in a linear regression model when some data on response variable is missing and the responses are equicorrelated. The ordinary least squares and optimal homogeneous predictors are employed to nd the imputed values of missing observations. Their eeciency properties are analyzed using the small disturbances asymptotic theory. ...

The creative capital possessed by the members of the creative class in region is either acquired through education or present innately in these members. Therefore, the creative infrastructure in the region is the sum of a part representing creative capital obtained through education and a second part denoting creative capital present innately in the creative class members. A researcher wishes t...

This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear probability model and provides sufficient conditions for unbiasedness and consistency to hold. The conditions suggest that a btrimming estimatorQ may reduce OLS bias. D 2005 Elsevier B.V. All rights reserved.

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 ...

AbstractIn this paper we formulate Stein–minimax type estimators and compare the performance properties with other estimators in case of mean value predictions. When the model is estimated by ordinary least squares it has been observed that least squares predicted is unbiased while minimax and Stein-minimax predictors are biased. The superiority conditions of the estimators have been derived by...

The Maximum Capture problem (MAXCAP) is a decision model that addresses the issue of location in a competitive environment. This paper presents a new approach to determine which store’s attributes (other than distance) should be included in the new Market Capture Models and how they ought to be reflected using the Multiplicative Competitive Interaction model. The methodology involves the design...