An objective approach to distinguish the investment strategies followed by different mutual funds is proposed. Tracking error variance is used as a measure of how actively a fund is managed and several decompositions of tracking error variance are proposed to identify different investment strategies. Furthermore, recognizing that tracking error variance is subject to sampling error, we provide ...

Ordinary least squares regression analysis is generally inappropriate for calibration and regression problems when the usual assumption ofconstant variance across all observations doesn't hold. Estimators of regression parameters are of relatively poor quality and the resulting inference can be misleading. The use of standard data transformations is a common alternative but may not provide enou...

The problem of covariate measurement error with heteroscedastic measurement error variance is considered. Standard regression calibration assumes that the measurement error has a homoscedastic measurement error variance. An estimator is proposed to correct regression coefficients for covariate measurement error with heteroscedastic variance. Point and interval estimates are derived. Validation ...

The bias-variance decomposition of error provides useful insights into the error performance of a classifier as it is applied to different types of learning task. Most notably, it has been used to explain the extraordinary effectiveness of ensemble learning techniques. It is important that the research community have effective tools for assessing such explanations. To this end, techniques have ...