Nonparametric Independence Tests in High-Dimensional Settings, with Applications to the Genetics of Complex Disease

• Autores: Fernando Castro Prado
• Directores de la Tesis: Wenceslao González Manteiga (dir. tes.) , Javier Costas Costas (dir. tes.)
• Lectura: En la Universidade de Santiago de Compostela ( España ) en 2024
• Idioma: inglés
• Tribunal Calificador de la Tesis: Rosa María Crujeiras Casais (presid.) , Mar Rodríguez Girondo (secret.) , Juan Ramón González Ruiz (voc.)
• Enlaces
  • Tesis en acceso abierto en: MINERVA
• Resumen

  • Nowadays, genetics studies large amounts of very diverse variables. Mathematical statistics has evolved in parallel to its applications, with much recent interest high-dimensional settings. In the genetics of human common disease, a number of relevant problems can be formulated as tests of independence. We show how defining adequate premetric structures on the support spaces of the genetic data allows for novel approaches to such testing. This yields a solid theoretical framework, which reflects the underlying biology, and allows for computationally-efficient implementations. For each problem, we provide mathematical results, simulations and the application to real data.