A0887
Title: Multi-response linear regression estimation based on low-rank pre-smoothing
Authors: Sandipan Roy - University of Bath (United Kingdom) [presenting]
Matthew Nunes - Lancaster University (United Kingdom)
Xinle Tian - University of Bath (United Kingdom)
Alex Gibberd - Lancaster University (United Kingdom)
Abstract: Pre-smoothing is a technique aimed at increasing the signal-to-noise ratio in data to improve subsequent estimation and model selection in regression problems. However, pre-smoothing has thus far been limited to the univariate response regression setting. Motivated by the widespread interest in multi-response regression analysis in many scientific applications, a technique for data pre-smoothing is proposed in this setting based on low-rank approximation. Theoretical results are established on the performance of the proposed methodology and quantify its benefit empirically in a number of simulated experiments. The proposed low-rank pre-smoothing technique is also demonstrated on real data arising from the environmental sciences.
