Aguiar Fernández, PabloKemp, Andrew HaddonArias López, Juan Antonio2025-12-092025-12-092025https://hdl.handle.net/10347/44309This doctoral thesis introduces a novel statistical framework to improve early detection of Alzheimer’s disease (AD) using neuroimaging data, particularly Positron Emission Tomography (PET). The proposed method is grounded in Functional Data Analysis (FDA), a statistical paradigm that treats data as continuous spatial surfaces rather than collections of independent voxel values. This shift allows for more powerful and spatially coherent inference, which is especially important in neuroimaging contexts and in early disease stages where subtle and diffuse changes are the norm.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/AlzheimerneuroimagingPETSCC240401 Bioestadísticadoctoral thesisopen access