Abstract

We propose a fast covariance smoothing method and associated software that scale up linearly to very large matrices. The main idea is to exploit a very fast new bivariate penalized spline smoothing approach and focus on the practicality and scalability of the method. Currently available methods and software cannot smooth covariance matrices of dimension J > 500, whereas our approach provides fast smoothing for matrices of dimension J > 10; 000. An R function, simulations, and data analysis provide ready to use, reproducible, and scalable tools for practical data analysis of noisy high-dimensional functional data.

Disciplines

Biostatistics | Statistical Methodology

Media Format

flash_audio