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Mixture-based Nonparametric Density Estimation

When:  16th of May 2017 tot 15th of May 2017
Starttime  12:45

On Tuesday (16/05/2017), we have another interesting talk in our Probability and Statistics seminar series at TU Delft.
All of you are very welcome.

 

Yong Wang (The University of Auckland)

When: Tuesday May 16th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Mixture-based Nonparametric Density Estimation

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.
Results of simulation studies and real-world data in both the univariate and the multivariate situation will be given, all suggesting that these mixture-based estimators outperform the kernel-based ones.

More details here: http://goo.gl/TXD1Sw
 
More information:  [comment]
Telephone:  [rooster_phone]
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