Tata Institute of Fundamental Research
Information Geometry of the Robust Maximum Mean Power Likelihood Estimation
STCS Seminar
Speaker:
Rajesh Sundaresan (Indian Institute of Science
Department of Electrical Communication Engineering
Bangalore 560012)
Organiser:
Vinod M. Prabhakaran
Date:
Tuesday, 20 Jan 2015, 13:45 to 14:45
Venue:
AG-69
(Scan to add to calendar)
Abstract:
Abstract: The maximum "mean power" likelihood estimation procedure (proposed by [Basu et al., Biometrika, 1998]) is a robust variant of the maximum likelihood estimation procedure. Given iid samples coming from an unknown distribution, a member of a given parametric family of distributions, the mean power likelihood estimation procedure is to find the parameter of the distribution that is closest, in a particular sense, to the empirical distribution of the samples. One may view the resulting distribution as a "projection" of the empirical distribution on the parametric family. In this talk, I will highlight the geometry associated with this projection. I will also discuss a simplified computation procedure, one that is suggested by the geometric view point, when the estimation is of the parameter of a power-law family (this is joint work with M. Ashok Kumar). arXiv:1410.5550
Biography: Rajesh Sundaresan is an associate professor at the ECE department of IISc Bangalore.
| Speaker: | Rajesh Sundaresan (Indian Institute of Science Department of Electrical Communication Engineering Bangalore 560012) |
| Organiser: | Vinod M. Prabhakaran |
| Date: | Tuesday, 20 Jan 2015, 13:45 to 14:45 |
| Venue: | AG-69 |
(Scan to add to calendar)
Abstract:
Abstract: The maximum "mean power" likelihood estimation procedure (proposed by [Basu et al., Biometrika, 1998]) is a robust variant of the maximum likelihood estimation procedure. Given iid samples coming from an unknown distribution, a member of a given parametric family of distributions, the mean power likelihood estimation procedure is to find the parameter of the distribution that is closest, in a particular sense, to the empirical distribution of the samples. One may view the resulting distribution as a "projection" of the empirical distribution on the parametric family. In this talk, I will highlight the geometry associated with this projection. I will also discuss a simplified computation procedure, one that is suggested by the geometric view point, when the estimation is of the parameter of a power-law family (this is joint work with M. Ashok Kumar). arXiv:1410.5550
Biography: Rajesh Sundaresan is an associate professor at the ECE department of IISc Bangalore.
Biography: Rajesh Sundaresan is an associate professor at the ECE department of IISc Bangalore.