2020/03/25 by Zhiyang Zhou, Zhou, Zhiyang, Richard Lockhart +1
Mathematics · Neuroscience · #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2003.11542
openalex publication_date 2020/03/25 · openalex created_date 2020/04/03 · openalex updated_date 2026/07/28
Functional partial least squares (FPLS) is commonly used for fitting scalar-on-function regression models. For the sake of accuracy, FPLS demands that each realization of the functional predictor is recorded as densely as possible over the entire time span; however, this condition is sometimes violated in, e.g., longitudinal studies and missing data research. Targeting this point, we adapt FPLS to scenarios in which the number of measurements per subject is small and bounded from above. The resulting proposal is abbreviated as PLEASS. Under certain regularity conditions, we establish the consistency of estimators and give confidence intervals for scalar responses. Simulation studies and real-data applications illustrate the competitive accuracy of PLEASS