Reducing bias and mean squared error associated with regression-based odds ratio estimators

Simple Adjustments to Reduce Bias and Mean Squared Error Associated With Regression-Based Odds Ratio and Relative Risk Estimators

Mean Integrated Squared Error of Nonlinear Wavelet-based Estimators with Long Memory Data

We consider the nonparametric regression model with long memory data that are not necessarily Gaussian and provide an asymptotic expansion for the mean integrated squared error (MISE) of nonlinear wavelet-based mean regression function estimators. We show this MISE expansion, when the underlying mean regression function is only piecewise smooth, is the same as analogous expansion for the kernel...

Shrinkage estimation-based source localization with minimum mean squared error criterion and minimum bias criterion

Article history: Available online 18 February 2014

Root Mean Squared Error

• Predictive Accuracy Measures. These measures evaluate how close the recommender system came to predicting actual rating/utility values. • Classification Accuracy Measures. These measures evaluate the frequency with which a recommender system makes correct/incorrect decisions regarding items. • Rank Accuracy Measures. These measures evaluate the correctness of the ordering of items performed b...

Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso

We study a simple linear regression problem for grouped variables; we are interested in methods which jointly perform estimation and variable selection, that is, that automatically set to zero groups of variables in the regression vector. The Group Lasso (GLasso), a well known approach used to tackle this problem which is also a special case of Multiple Kernel Learning (MKL), boils down to solv...