Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled....

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many...

Motivated by the fact that a linear speci fication in a quantile regression setting is unable to describe the non-linear relations among economic variables, as documented in the empirical econometrics literature, we are the first to formulate and analyze a multiple threshold quantile regression...

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semiparametric general trimmed estimator (GTE) of truncated...