TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Clancy, Richard J.
A1  - Becker, Stephen
T1  - Approximate maximum likelihood estimators for linear regression with independent component-wise design matrix uncertainty
JF  - Mathematical Programming Computation
N2  - In this paper we consider regression problems subject to 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 independent component-wise uncertainty in the design matrix. To address difficulties encountered when dealing with distributions of sums of random variables, we rely on the saddle point method to estimate densities and form an approximate log-likelihood to maximize. We show that the proposed method performs favorably against other classical methods.
AB  - In this paper we consider regression problems subject to 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 independent component-wise uncertainty in the design matrix. To address difficulties encountered when dealing with distributions of sums of random variables, we rely on the saddle point method to estimate densities and form an approximate log-likelihood to maximize. We show that the proposed method performs favorably against other classical methods.
Y1  - 2024
SN  - 1867-2949
SS  - 1867-2949
U6  - https://doi.org/10.1007/s12532-024-00268-6
DO  - https://doi.org/10.1007/s12532-024-00268-6
VL  - 17
IS  - 1
SP  - 53
EP  - 79
S1  - 27
PB  - Springer Science and Business Media LLC
ER  -