Title :
Entropy minimization for parameter estimation problems with unknown distribution of the output noise
Author :
Pronzato, L. ; Thierry, É
Author_Institution :
Lab. I3S, CNRS, Sophia Antipolis, France
Abstract :
We consider the situation where the parameters θ of a linear regression model have to be estimated from observations corrupted by an additive noise with unknown distribution f. Since maximum likelihood estimation cannot be used, we estimate θ by minimizing the entropy of a kernel estimate of f, constructed from the residuals. An example of parameter estimation in the presence of interference with random binary signals is presented
Keywords :
interference suppression; minimum entropy methods; parameter estimation; random processes; statistical analysis; additive noise; interference; kernel estimate; linear regression model; minimum entropy; observations; output noise distribution; parameter estimation; random binary signals; Additive noise; Ear; Entropy; H infinity control; Interference; Kernel; Linear regression; Maximum likelihood estimation; Parameter estimation; Probability density function;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940719
