In statistics, it is important to have realistic data sets available for...
While there exists several inferential methods for analyzing functional ...
This contribution analyzes the self-perception and political biases of
O...
Data cubes are multidimensional databases, often built from several sepa...
Many planning and decision activities in logistics and supply chain
mana...
When creating multi-channel time-series datasets for Human Activity
Reco...
Analysts seldom include interaction terms in meta-regression model, what...
Background: The COVID-19 pandemic has had a profound impact on health,
e...
Multivariate analysis-of-variance (MANOVA) is a well established tool to...
Many production processes are characterized by numerous and complex
caus...
Studies to compare the survival of two or more groups using time-to-even...
Correlation matrices are an essential tool for investigating the depende...
While there appears to be a general consensus in the literature on the
d...
In repeated Measure Designs with multiple groups, the primary purpose is...
Meta-analyses frequently include trials that report multiple effect size...
In this work we present a rigorous application of the Expectation
Maximi...
Mixed-effects meta-regression models provide a powerful tool for evidenc...
Tree-based ensembles such as the Random Forest are modern classics among...
Missing covariates in regression or classification problems can prohibit...
A pandemic poses particular challenges to decision-making with regard to...
A key task in multi-label classification is modeling the structure betwe...
In many life science experiments or medical studies, subjects are repeat...
In statistical survey analysis, (partial) non-responders are integral
el...
The research on and application of artificial intelligence (AI) has trig...
Meta-analyses of correlation coefficients are an important technique to
...
Factorial survival designs with right-censored observations are commonly...
In this paper, we consider the task of predicting travel times between t...
We propose inference procedures for general nonparametric factorial surv...
Population means and standard deviations are the most common estimands t...
The issue of missing values is an arising difficulty when dealing with p...
Variable selection in sparse regression models is an important task as
a...
We introduce a unified approach to testing a variety of rather general n...
In applied research, it is often sensible to account for one or several
...
The issue of estimating residual variance in regression models has
exper...
The Welch-Satterthwaite t-test is one of the most prominent and often us...
We introduce novel wild bootstrap procedures for testing superiority in
...
We propose new resampling-based approaches to construct asymptotically v...
This paper introduces new effect parameters for factorial survival desig...
Imputation procedures in biomedical fields have turned into statistical
...
Rank-based inference methods are applied in various disciplines, typical...
We consider statistical procedures for hypothesis testing of real valued...
The numerical availability of statistical inference methods for a modern...
Multivariate analysis of variance (MANOVA) is a powerful and versatile m...
Missing data is an expected issue when large amounts of data is collecte...
Split-plot or repeated measures designs are frequently used for planning...