This monograph grew out of joint work with various dedicated colleagues and students at
the Vienna Institute for Advanced Studies. We would probably never have begun without the …

… Such models are called linear models. Most commonly, … y given X is expressed as a linear
function of X. Like all forms of regression analysis, linear regression focuses on the conditional …

… regression models. The choice between the use of the ordinal regression model or a linear
regression model … On the one hand, the proportional odds ordinal regression model appears …

… criteria in the linear multivariate regression model, and … of Klein's Model I of the United
States economy, 1921-1941. … hypotheses in the multivariate linear regression model, and then …

… model uncertainty in linear regression models. Conditioning on a single selected model ignores
model … A Bayesian solution to this problem involves averaging over all possible models (…

… Regression models are often employed in much the same way—for both practical
decision-making and more theoretical or scientific enquiry—when applied both to medicine in …

… 4 in terms of a simple conceptual model for the rainfall-runoff relationship and to … model for
arid or semiarid watersheds. The model proposed will be called the linear regression model to …

… The statistical techniques can be evaluated for the predictive model based on the …
regression models. In this paper, linear regression and support vector regression model is …

… residuals can also be used to decide upon directional dependence in the multiple linear
regression setting, if we consider that the correctly specified regression model assumes …

… We consider the semiparametric linear regression model with censored data and with …
We show the equivalence between this type of estimator and an estimator based on a linear …