RT Dissertation/Thesis
T1 Nonparametric Independence Tests in High-Dimensional Settings, with Applications to the Genetics of Complex Disease
A1 Castro Prado, Fernando
K1 Xenómica
K1 estatística en alta dimensión
K1 espazos métricos
K1 métodos non paramétricos
AB Nowadays, genetics studies large amounts of very diversevariables. Mathematical statistics has evolved in parallel to itsapplications, with much recent interest high-dimensionalsettings. In the genetics of human common disease, a numberof relevant problems can be formulated as tests ofindependence. We show how defining adequate premetricstructures on the support spaces of the genetic data allows fornovel approaches to such testing. This yields a solid theoreticalframework, which reflects the underlying biology, and allowsfor computationally-efficient implementations. For eachproblem, we provide mathematical results, simulations and theapplication to real data.
YR 2024
FD 2024
LK http://hdl.handle.net/10347/34959
UL http://hdl.handle.net/10347/34959
LA eng
DS Minerva
RD 24 abr 2026