Partial Functional Linear Quantile Regression for Neuroimaging Data Analysis

TL;DR

This paper introduces a new prediction method for functional linear quantile regression using partial quantile covariance, along with an efficient algorithm for basis extraction, extending to composite quantile regression, and demonstrates its effectiveness on simulated and real neuroimaging data.

Contribution

It develops a novel partial quantile covariance technique, a simple algorithm for basis extraction, and extends the approach to composite quantile regression, improving analysis of neuroimaging data.

Findings

Proposed methods outperform existing techniques in simulations.

Effective analysis of ADHD-200 and ADNI neuroimaging datasets.

Algorithms are computationally efficient and accurate.

Abstract

We propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression (SIMPQR) algorithm to efficiently extract partial quantile regression (PQR) basis for estimating functional coefficients. We further extend our partial quantile covariance techniques to functional composite quantile regression (CQR) defining partial composite quantile covariance. There are three major contributions. (1) We define partial quantile covariance between two scalar variables through linear quantile regression. We compute PQR basis by sequentially maximizing the partial quantile covariance between the response and projections of functional covariates. (2) In order to efficiently extract PQR basis, we develop a SIMPQR algorithm analogous to simple partial least squares (SIMPLS). (3) Under the homoscedasticity assumption, we extend our techniques to partial composite quantile covariance and use it to find the partial composite quantile regression (PCQR) basis. The SIMPQR algorithm is then modified to obtain the SIMPCQR algorithm. Two simulation studies show the superiority of our proposed methods. Two real data from ADHD-200 sample and ADNI are analyzed using our proposed methods.