Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Bodhi Sen

Bodhi Sen
Bodhi Sen
Columbia University
Caldwell Hall Room 102
Bodhi Sen.docx (64.1 KB)

Nonparametric Estimation of a Two-component Mixture Model with application to Multiple Testing   

We consider estimation and inference in a two-component mixture model where the distribution of one component is completely unknown. We develop methods for estimating the mixing proportion and

the unknown distribution nonparametrically, given i.i.d. data from the mixture model. We use ideas from shape restricted
function estimation and develop "tuning parameter-free" estimators that are easily

implementable and have good finite sample performance. We establish the consistency of our procedures. Distribution-free finite sample lower confidence bounds are developed for the mixing proportion. 

We next consider the problem of multiple testing when additional covariate information is available on each of the hypothesis tests. We propose a model for such data and develop likelihood based methods for estimating the unknown parameters. The theoretical properties of the proposed estimators are studied. We illustrate the practical efficacy of our methodology in applications in neuroscience, astronomy and genomics.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.