Estimation of semi-functional linear models by wavelets

Abstract

The advances of technology and computing have helped in solution of many statistical problems. In particular, the functional data analysis (FDA) as relatively modern branch of statistics, have benefited from such advances. In the scope of FDA, the functional regression models have gained special attention by researchers, because of their wide applicability in real-life problems. In this project, we have the interest of exploring the estimation of semi-functional linear models by wavelet methodology and compare the obtained results with published counterpart standard methods. We also plan to explore semi-functional generalized linear models (GLM) in the multiscale domains. (AU)