2021
DOI: 10.48550/arxiv.2104.03307
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Approximate maximum likelihood estimators for linear regression with design matrix uncertainty

Abstract: In this paper we consider regression problems subject to arbitrary noise in the operator or design matrix. This characterization appropriately models many physical phenomena with uncertainty in the regressors. Although the problem has been studied extensively for ordinary/total least squares, and via models that implicitly or explicitly assume Gaussianity, less attention has been paid to improving estimation for regression problems under general uncertainty in the design matrix. To address difficulties encount… Show more

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References 24 publications
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