Statistical estimates from survey samples have traditionally been obtained via design-based
estimators. In many cases, these estimators tend to work well for quantities such as …

Variable selection plays a key role in modern high-dimensional statistics. This dissertation
provides a comprehensive survey of theory and methods developed by the author and …

The recent surge in large-scale population health datasets, such as the UK Biobank or the
Adolescent Brain Cognitive Development (ABCD) study, requires the development of …

High-throughput sequencing (HTS) techniques such as RNA-seq, ChIP-seq and ATAC-seq
have enabled researchers to investigate complex biological processes in unprecedented …

It is widely acknowledged that loan companies face various risks, and there are several
models available to help them analyze customer behavior and control these risks. My …

In Bayesian statistics, routinely implemented deterministic approximations of posterior
distributions typically rely on symmetric densities, often taken to be Gaussian. Such a choice …

Urbanization has caused a historical transformation at a global scale, and humanity is
moving towards a fully connected society where cities will concentrate population …

Probit and logistic regressions are among the most popular and well-established
formulations to model binary observations, thanks to their plain structure and high …

Al giorno d'oggi, la crescente dimensionalità e complessità dei dati generati da problemi
applicativi reali pone nuove sfide teoriche e pratiche ai ricercatori che operano in qualsiasi …

Nowadays, the increasing dimension and complexity of real data problems pose hard
theoretical and practical challenges to researchers working in any field of science. The study …