A minimax approach to errors-in-variables linear models
type: Item
instance of: scholarly article
title: A minimax approach to errors-in-variables linear models
zbMATH Open document ID: 1412.62097
DOI: 10.3103/S1066530718030031
author: Y. Aharonov
published in: Mathematical Methods of Statistics
publication date: 2018-12-05T00:00:00Z
review text: The author considers a simple linear regression model with Gaussian noise in both variables (regressor and regressand). The goal is to estimate the two unknown regression parameters. The classical maximum likelihood estimates show an unstable behaviour for large noise levels, e.g. unbounded moments. To overcome this disadvantage the author introduces a minimax approach and finds estimates with much more better behaviour under large noise regime. To construct these estimates a fast algorithm is proposed which solves the underlying convex optimization problem.
reviewed by: Wolfgang Näther
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62C20
Mathematics Subject Classification ID: 52B55
zbMATH DE Number: 6989594
zbMATH Keywords: convex optimization problem
MaRDI profile type: Publication
cites work: Q4117258
cites work: Q3996777
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cites work: Applications of the van Trees inequality: A Bayesian Cramér-Rao bound
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full work available at URL: s1066530718030031
OpenAlex ID: W2895790556
Wikidata QID: Q129085380
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description: scientific article; zbMATH DE number 6989594