Browse Items (10 total)

Contemporary empirical applications frequently require flexible regression models for
complex response types and large tabular or non-tabular, including image or text, data.
Classical regression models either break down under the computational load…

The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes nonlinear components, interactions, or
transformations. Analysts who fit such complex models often seek to transform raw…

The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks’ predictions with so-called feature attribution methods.
Aside from the unified and user-friendly framework, the package stands out…

Spatial and spatiotemporal machine-learning models require a suitable framework for
their model assessment, model selection, and hyperparameter tuning, in order to avoid
error estimation bias and over-fitting. This contribution provides an overview…

Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning, and related fields. In the field of productivity
and efficiency analysis, recent developments in multivariate convex…

Birth-and-death processes (BDPs) form a class of continuous-time Markov chains that
are particularly suited to describing the changes in the size of a population over time.
Population-size-dependent BDPs (PSDBDPs) allow the rate at which a…

We describe the R package BEKKs, which implements the estimation and diagnostic
analysis of a prominent family of multivariate generalized autoregressive conditionally heteroskedastic (MGARCH) processes, the so-called BEKK models. Unlike existing…

This work describes the R package GET that implements global envelopes for a general
set of d-dimensional vectors T in various applications. A 100(1−α)% global envelope is a
band bounded by two vectors such that the probability that T falls outside…

A Bayesian network is a multivariate (potentially very high dimensional) probabilistic
model formed by combining lower-dimensional components. In Bayesian networks, the
computation of conditional probabilities is fundamental for model-based…

The analysis of clinical significance is helpful to decide if an intervention leads to practically relevant or meaningful changes for individual patients which is clearly different
from the analysis of statistical significance. However, the…