Year: 2013
Type: article
Abstract: We consider, in the modern setting of high-dimensional statistics, the classic problem of optimizing the objective function in regression using M-estimates when the error distribution is assumed to be known. We propose an algorithm to compute this optimal objective function that takes into account the dimensionality of the problem. Although optimality is achieved under assumptions on the design matrix that will not always be satisfied, our analysis reveals generally interesting families of dimension-dependent objective functions.
Institution: University of California, Berkeley
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: G1513559549, EMSW21-VIGRE Berkeley Statistics VIGRE Program, CAREER: Random matrices and High-dimensional statistics, Emerging Frontiers of Science of Information, Sparse and structured networks: Statistical theory and algorithms, CDI Type II: Collaborative Research: Sparse Inference: New Tools for Structural Knowledge Discovery
OpenAlex ID: W2132235473