Regression and linear programming provide the basis for popular techniques for estimating technical efficiency. Regression based approaches are typically parametric and can be both deterministic or stochastic where the later allows for measurement error. In contrast, linear programming models are nonparametric and allow multiple inputs and outputs. The purported disadvantage of the regression b...

the quantitative structure–property relationship (qspr) method is used to develop the correlation between structures of crude oil hydrocarbons (80 compounds) and their boiling point and water solubility. sub-structural molecular fragments (smf) calculated from structure alone were used to represent molecular structures. a subset of the calculated fragments selected using stepwise regression (fo...

Linear regression is arguably one of the most widely used statistical methods. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs) in the linear regression context. In general, the IM framework is able to produce meaningful probabilistic summaries of the sta...

The general setting of regression analysis is to identify a relationship between a response variable Y and one or several explanatory variables X by using a learning sample. In a prediction framework, the main assumption for predicting Y on a new sample of X observations is that the regression model Y = f(X) + ǫ is still valid. Unfortunately, this assumption is not always true in practice and t...