While traditional multiple testing procedures prohibit adaptive analysis choices made by users, Goeman and Solari (Statist. Sci. 26 (2011) 584–597) proposed a simultaneous inference framework that ...

In most applications, regression models are merely useful approximations. Reality is often so complicated that you cannot know what the true model is. You may have to choose a model more on the basis ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...

After fitting survival data with a linear regression model, it is important to know how to use the results to make prediction of the t-year survival probability or median failure time for future ...

Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...

In this paper, we propose a functional linear regression model in the space of probability density functions. We treat a cross-sectional distribution of individual earnings as an infinite dimensional ...

This course is available on the MSc in Quantitative Methods for Risk Management. This course is available with permission as an outside option to students on other programmes where regulations permit.