Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging. (arXiv:2205.14220v2 [stat.ME] UPDATED)

Multi-task learning is frequently used to model a set of related response
variables from the same set of features, improving predictive performance and
modeling accuracy relative to methods that handle each response variable
separately. Despite the potential of multi-task learning to yield more powerful
inference than single-task alternatives, prior work in this area has largely
omitted uncertainty quantification. Our focus in this paper is a common
multi-task problem in neuroimaging, where the goal is to understand the
relationship between multiple …

applications arxiv inference neuroimaging regression