In statistical inference hypotheses related to different kinds of phenomena are formulated,
and then data are collected and analyzed, which either confirm or falsify these hypotheses …

One of the most important aspects of the (statistical) analysis of imprecise data is the usage
of a suitable distance on the family of all compact, convex fuzzy sets, which is not too hard to …

In recent years, model averaging, by which estimates are obtained based on not one single
model but a weighted ensemble of models, has received growing attention as an alternative …

Several books written by prominent authors during the last 40 years have covered different
aspects of random sets rather independently of one another. In most cases, random sets are …

Real-life data associated with experimental outcomes are not always real-valued. In
particular, opinions, perceptions, ratings, etc., are often assumed to be vague in nature …

The estimation of a simple linear regression model when both the independent and
dependent variable are interval valued is addressed. The regression model is defined by …

Different approaches to deal with regression analysis when the data are fuzzy are
presented. It summarizes recent results and considers them in a more general context which …

We study a variant of online multiclass classification where the learner predicts a single
label but receives a\textit {set of labels} as feedback. In this model, the learner is penalized …

The expected value of a fuzzy random variable plays an important role as central summary
measure, and for this reason, in the last years valuable statistical inferences about the …

Likert scales or associated codings are often used in connection with opinions/valuations/
ratings, and especially with questionnaires with a pre-specified response format. A guideline …