Theoretical Statistics and Mathematics Unit, ISI Delhi

July 23, 2014 (Wednesday) ,
3:30 PM at Webinar

Speaker:
Kaustav Nandy,
Indian Statistical Institute, Delhi

Title:
Semi-parametric Mixture Density Modeling

Abstract of Talk

Modeling mixture distributions is a well studied problem in
statistics. In this talk we consider a special case where the data
come from a mixture of an arbitrary decreasing density and a
parametric density, a model that has applications in particle physics
and astrophysics. As is common with mixture modeling, we use the EM
algorithm to find maximum likelihood estimates, using a variant of
Grenander's least concave majorant estimator to estimate the
decreasing density. Simulation is used to explore the performance of
our estimators, and challenges involved in establishing their
asymptotic properties are discussed briefly. We also discuss
modifications required in the case of binned data, which is common in
applied settings, and has the advantage that the model reduces to a
finite-dimensional one. This is joint work with Deepayan Sarkar.