Purpose of this study is to investigate the relation between board monitoring and firm performance after controlling the endogeneity and multicollinearity problem that exist in most corporate governance research. Prior studies failed to establish any significant relationship between board monitoring and firm performance because of not properly control for endogeneity and multicollinearity probl...

Conventional neuroimaging techniques provide information about condition-related changes of the BOLD (blood-oxygen-level dependent) signal, indicating only where and when the underlying cognitive processes occur. Recently, with the help of a new approach called "model-based" functional neuroimaging (fMRI), researchers are able to visualize changes in the internal variables of a time varying lea...

The Liu estimator has consistently been demonstrated to be an attractive shrinkage method for reducing the effects of multicollinearity. The Poisson regression model is a well-known model in applications when the response variable consists of count data. However, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator (MLE) of the Poisson regressio...

This paper proposes a method for eliminating multicollinearity from linear regression models. Specifically, we select the best subset of explanatory variables subject to the upper bound on the condition number of the correlation matrix of selected variables. We first develop a cutting plane algorithm that, to approximate the condition number constraint, iteratively appends valid inequalities to...

The most popular form of arti cial neural network, feedforward networks with sigmoidal activation functions, and a new statistical technique, multivariate adaptive regression splines (MARS) can both be classi ed as nonlinear, nonparametric function estimation techniques, and both show great promise for tting general nonlinear multivariate functions. In comparing the two methods on a variety of ...