PLACE: 1810 Gilman

SPEAKER:
Feifang Hu
Cornell University and Department of Statistics and Applied Probability National University of Singapore, Singapore

TITLE:
Markov Chain Marginal Bootstrap (MCMB)

ABSTRACT:

Markov chain marginal bootstrap (MCMB) is a new method for constructing
confidence intervals or regions for maximum likelihood estimators of certain
parametric models and for a wide class of M-estimators of linear regression.
The MCM bootstrap distinguishes itself from the usual bootstrap methods in two
important aspects:

 (i). it only involves solving one-dimensional equations for parameters of any
dimension;

 (ii). it  produces a Markov chain rather than a (conditionally) independent
sequence.

It is designed to alleviate computational burdens often associated with
bootstrap in high dimensional problems. The validity of MCMB is established
through asymptotic analyses and illustrated with empirical studies for linear
regression and generalized linear models. Some further research topics are also
discussed.
 
 

COFFEE: 3:45 p.m., 104 Snedecor