A special feature of Probability and Statistics Day at UMBC 2013 is that the conference, including the workshop, is open to all statistics graduate students
from UMBC and local universites free of charge; however, REGISTRATION IS REQUIRED! The deadline to register is Friday,
April 12, 2013. // REGISTER NOW
For more information, contact any member of the organizing committee:
Bimal Sinha
Conference Chair
443.538.3012
Kofi Adragni
410.455.2406
Yvonne Huang
410.455.2422
Yaakov Malinovsky
410.455.2968
Thomas Mathew
410.455.2418
Nagaraj Neerchal
410.455.2437
DoHwan Park
410.455.2408
Junyong Park
410.455.2407
Anindya Roy
410.455.2435
Elizabeth Stanwyck
410.455.5731
Participant Information
Deepak Nag Ayyala
Paper: Mean vector testing for dependent observations
Multivariate analysis has undergone radical changes in the recent past with the advent of the so-called ultra high-dimensional data sets. Standard procedures cannot be applied for analysis of such data sets as they are all developed based on the assumption that the sample size is larger than the dimension of the data. Two different families of tests have been proposed so far for mean vector testing in high-dimensional case, but they work only when the observations are assumed to be independently and identically distributed. We propose a new testing procedure when the observations are dependent. Asymptotic normality of the proposed test statistic is derived under the assumption that the data is a realization of a M -dependent strictly stationary process. The proposed test is also extended to the two sample case.
Poster: Statistical validation of reproducibility of Resting State Networks
The strength of resting state networks (RSN) can be determined by calculating the cross-correlation matrix among the signals from different regions of interest involved in the network. Likelihood ratio based tests that test the significance of the correlations assume independence amongst the observations. Since fMRI images recorded over time for a single subject are dependent, existing methods may lead to erroneous significance results for testing any hypothesis regarding the resting state networks. In this work, we propose a framework for testing the equality of correlation matrices at lag zero that can be used to validate an RSN. We use the framework to construct a multivariate model for RSN data that allows us to investigate reproducibility of networks over several visits and also test for effectiveness of several filtering methods used in pre-processing stage.
