Browse Items (8208 total)

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…

When predicting future events, it is common to issue forecasts that are probabilistic,
in the form of probability distributions over the range of possible outcomes. Such forecasts
can be evaluated using proper scoring rules. Proper scoring rules…

Multivariate spatio-temporal data refers to multiple measurements taken across space
and time. For many analyses, spatial and time components can be separately studied: for
example, to explore the temporal trend of one variable for a single spatial…

The sparse group lasso is a high-dimensional regression technique that is useful for
problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we…