Many efforts have been devoted, in recent times, to the study of statistical inference on curves. In this project, our aim is to develop new theoretical methodologies in order to make inference on curves, with applications in different frameworks: finite populations, goodness–of–fit testing, neural networks, machine learning, functional data analysis, set estimation, spatial and spatio–temporal statistics and finance series. Many of the new methods to be developed, will be implemented for the first time. They may also represent modifications on previous versions and their validity will be tested by theoretical results and simulation studies. Applications will be made on different settings as environmental science (SO2 predictions in a power plant, concentration of heavy metals in mosses in Galicia,…), Economy (prediction of the home income at nut3 level in Galicia,… ), Industry (control of residual treatment by anaerobic processes) and Finance (semiparametric inference in portfolio design,…).

Presentation of the People