**cshalizi + density_estimation + samworth.richard**
1

"Independent Component Analysis (ICA) models are very popular semiparametric models in which we observe independent copies of a random vector X=AS, where A is a non-singular matrix and S has independent components. We propose a new way of estimating the unmixing matrix W=A−1 and the marginal distributions of the components of S using nonparametric maximum likelihood. Specifically, we study the projection of the empirical distribution onto the subset of ICA distributions having log-concave marginals. We show that, from the point of view of estimating the unmixing matrix, it makes no difference whether or not the log-concavity is correctly specified. The approach is further justified by both theoretical results and a simulation study."

- By "heard the talk", I mean Richard was kind enough to explain this to me.

to:NB
independent_component_analysis
nonparametrics
statistics
density_estimation
samworth.richard
- By "heard the talk", I mean Richard was kind enough to explain this to me.

february 2013 by cshalizi

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