Abstract Surrogate Safety Measures (SSMs) are used to express road safety in terms of the
safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future …

Because of their high electrocatalytic activity, sensitivity, selectivity, and long-term stability in
electrochemical sensors and biosensors, numerous nanomaterials are being used as …

The Copula is widely used to describe the relationship between the marginal distribution
and joint distribution of random variables. The estimation of high-dimensional Copula is …

Abstract Machine learning (ML) is increasingly being used in official statistics with a range of
different applications. The main focus of ML models is to accurately predict attributes of new …

Nonparametric estimation for a probability density function that describes multivariate data
has typically been addressed by kernel density estimation (KDE). A novel density estimator …

Albeit worryingly underrated in the recent literature on machine learning in general (and, on
deep learning in particular), multivariate density estimation is a fundamental task in many …

Quantifying the uncertainty of a financial portfolio is important for investors and regulatory
agencies. Reporting such uncertainty accurately is challenging due to time-dependent …

Nonparametric estimation of copula density functions using kernel estimators presents
significant challenges. One issue is the potential unboundedness of certain copula density …

Probability mass function (PMF) estimation using a low-rank model for the PMF tensor has
gained increased popularity in recent years. However, its performance evaluation relied …

Financial markets are inherently fraught with uncertainty, translating directly into various
forms of risk. Among these, model risk—the risk associated with making poor decisions …