Soft maximin estimation for heterogeneous data
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Dokumenter
- Fulltext
Forlagets udgivne version, 20,4 MB, PDF-dokument
Extracting a common robust signal from data divided into heterogeneous groups is challenging when each group—in addition to the signal—contains large, unique variation components. Previously, maximin estimation was proposed as a robust method in the presence of heterogeneous noise. We propose soft maximin estimation as a computationally attractive alternative aimed at striking a balance between pooled estimation and (hard) maximin estimation. The soft maximin method provides a range of estimators, controlled by a parameter (Formula presented.), that interpolates pooled least squares estimation and maximin estimation. By establishing relevant theoretical properties we argue that the soft maximin method is statistically sensible and computationally attractive. We demonstrate, on real and simulated data, that soft maximin estimation can offer improvements over both pooled OLS and hard maximin in terms of predictive performance and computational complexity. A time and memory efficient implementation is provided in the R package SMME available on CRAN.
Originalsprog | Engelsk |
---|---|
Tidsskrift | Scandinavian Journal of Statistics |
Vol/bind | 49 |
Udgave nummer | 4 |
Sider (fra-til) | 1761-1790 |
ISSN | 0303-6898 |
DOI | |
Status | Udgivet - 2022 |
Bibliografisk note
Publisher Copyright:
© 2022 The Authors. Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics.
Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk
ID: 308487811