A special feature of Probability and Statistics Day at UMBC 2014 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 11, 2014. // 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
Ritwik Mitra
Paper: Multivariate analysis of nonparametric estimates of correlation matrices
We study concentration in spectral norm of nonparametric estimates of correlation matrices. We work within the con fines of a Gaussian copula model. Two nonparametric estimates of elements of the correlation matrix, derived via sin transformations of the Kendall's tau and Spearman's rho correlation coefficient, are studied. Expected spectrum error bound is obtained for both the estimates. In addition, a general large deviation bound for the maximum spectral error of any s-dimensional submatrix is also derived. These results prove that when both number of variables d and sample size n are large, the spectral error bound of the nonparametric estimates matches the sharpest known rate of convergence given by the latent sample correlation matrix estimator. As an application, we establish the minimax optimal convergence rate in estimation of high dimensional bandable correlation matrices via tapering off of these nonparametric estimates. As another application, minimax optimal convergence rate for sparse principal component analysis is also established.
