A minimum-entropy estimator for regression problems with unknown distribution of observation errors

Details A minimum-entropy estimator for regression problems with unknown distribution of observation errors A minimum-entropy estimator for regression problems with unknown distribution of observation errors

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..169p&link_type=abstract

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 20th International Workshop. AIP Conference Proceedi

Mathematics

Probability

Probability Theory, Information Theory And Communication Theory, Data Analysis: Algorithms And Implementation, Data Management

Scientific paper

We consider a nonlinear regression model, with independent observation errors identically distributed with an unknown probability density function f(.). Instead of minimizing the empirical version of the entropy of f(.) based on the residuals, which corresponds to maximum likelihood estimation and requires the knowledge of f(.), we minimize the entropy of a (symmetrized) kernel estimate fcircn,h of f(.), constructed from the residuals. Two examples are presented to illustrate the finite-sample behavior of this estimator (accuracy, robustness). Some (preliminary) consistency results are given. .

Affiliated with

Also associated with

No associations

Rating

A minimum-entropy estimator for regression problems with unknown distribution of observation errors does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A minimum-entropy estimator for regression problems with unknown distribution of observation errors, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A minimum-entropy estimator for regression problems with unknown distribution of observation errors will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-924024