Functional data analysis (FDA) involves the analysis of data whose ideal units of
observation are functions defined on some continuous domain, and the observed data …

Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …

The analysis of samples of random objects that do not lie in a vector space is gaining
increasing attention in statistics. An important class of such object data is univariate …

This article considers minimax and adaptive prediction with functional predictors in the
framework of functional linear model and reproducing kernel Hilbert space. Minimax rate of …

State of charge (SOC) and state of health (SOH) estimation play a vital role in battery
management systems (BMSs). Accurate and robust state estimation can prevent Li-ion …

Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under
active study in the field of cancer biology. Its rapid progression and the relative time cost of …

We introduce the functional generalized additive model (FGAM), a novel regression model
for association studies between a scalar response and a functional predictor. We model the …

Supplement to “Estimation in functional linear quantile regression”. This supplementary file
contains the additional discussion on the connection to nonlinear ill-posed inverse …

The use of imaging markers to predict clinical outcomes can have a great impact in public
health. The aim of this article is to develop a class of generalized scalar-on-image …