Statistics and Its Interface

Volume 15 (2022)

Number 2

Sparse logistic regression on functional data

Pages: 171 – 179



Pang Du (Virginia Tech)

Yunnan Xu (Novartis International AG)

John Robertson (Virginia Tech)

Ryan Senger (Virginia Tech)


Motivated by a hemodialysis monitoring study, we propose a logistic model with a functional predictor, called the Sparse Functional Logistic Regression (SFLR), where the corresponding coefficient function is locally sparse, that is, it is completely zero on some subregions of its domain. The coefficient function, together with the intercept parameter, are estimated through a doubly-penalized likelihood approach with a B-splines expansion. One penalty is for controlling the roughness of the coefficient function estimate and the other penalty, in the form of the $L_1$ norm, enforces the local sparsity. A Newton–Raphson procedure is designed for the optimization of the penalized likelihood. Our simulations show that SFLR is capable of generating a smooth and reasonably good estimate of the coefficient function on the non-null region(s) while recognizing the null region(s). Application of the method to the Raman spectral data generated from the hemodialysis study pinpoint the wavenumber regions for identifying key chemicals contributing to the dialysis progress.


generalized functional linear model, local sparsity, penalized likelihood

This research was supported in part by the U.S. National Science Foundation grants DMS-1620945 and DMS-1916174.

Received 27 November 2020

Accepted 16 June 2021

Published 11 January 2022