Bayesian Statistics is typically taught, if at all, separately from conventional frequentist methods. It is becoming clear, however, that the emergence of powerful objective Bayesian methods (where the result, as in frequentist statistics, only depends on the assumed model and the observed data) provides a new unifying perspective on most established methods, and may be used in situations (e.g....

It is shown in detail how recent advances in multiple-output and projectional quantile regression open the door to exact computation of many inferential statistics based on projection pursuit. This is also illustrated on a few examples including new regression generalizations of multivariate skewness, kurtosis and projection depth. Note: The following files were submitted by the author for peer...

Complex longitudinal data are commonly analyzed using nonlinearmixed-effects NLME models with a normal distribution. However, a departure fromnormalitymay lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures ca...

In this paper I argue how we can impart deep understanding of inferential statistics by the early introduction of two approaches that students normally encounter only late in their studies, if at all. These are non-parametric methods and ANOVA. As illustration, we imagine a marathon race where firstly there are just two teams with two runners each, and secondly where there is a third team with ...

This paper presents the nonlinear IV methodology as an effective inferential basis for nonstationary panels. The nonlinear IV method resolves the inferential difficulties in testing for unit roots arising from the intrinsic heterogeneities and cross-dependencies of panel models. Individual units are allowed to be dependent through correlations among innovations, interrelatedness of short-run dy...