Variable selection methods and model selection approaches are valuable statistical tools
that are indispensable for almost any statistical modeling question. This review first …

Should one rely on a parametric or nonparametric model when analysing a given data set?
This classic question cannot be answered by traditional model selection criteria like AIC and …

We consider the problem of model selection for quantile regression analysis when a
particular purpose of the modeling procedure has to be taken into account. Typical …

Standard approaches to risk management focus on short-run risks, yet many positions are
held for longer periods. Over such holding periods there is a risk that risks will change. In …

In statistical research there usually exists a choice between structurally simpler or more
complex models. We argue that, even if a more complex, locally stationary time series model …

A central point of disagreement, in certain long-standing discussions about a particular
whaling dataset in the Scientific Committee of the International Whaling Commission, has …

Multiplicative Error Models (MEM) can be used to trace the dynamics of non-negative valued
processes. Interactions between several such processes are accommodated by the vector …

Within models for nonnegative time series, it is common to encounter deterministic
components (trends, seasonalities) which can be specified in a flexible form. This work …

This paper develops a new approach for order selection in autoregressive moving average
models using the focused information criterion. This criterion minimizes the asymptotic mean …

The quest for a good estimator of a certain focus or target is present regardless of the
dimensionality of the data. Obtaining such a good estimator with low mean squared error …