Mixture-based Nonparametric Density Estimation
|When:||16th of May 2017 tot 15th of May 2017|
On Tuesday (16/05/2017), we have another interesting talk in our Probability and Statistics seminar series at TU Delft.
Yong Wang (The University of Auckland)
When: Tuesday May 16th, 12:45
In this talk, I will describe a general framework for nonparametric density estimation that uses nonparametric or semiparametric mixture distributions. Similar to kernel-based estimation, the proposed approach uses bandwidth to control the density smoothness, but each density estimate for a fixed bandwidth is determined by likelihood maximization, with bandwidth selection carried out as model selection. This leads to much simpler models than the kernel ones, yet with higher accuracy.
More details here: http://goo.gl/TXD1Sw