Dr. Sreenivas Konda (UCSB)
Title: Consistency of Large Autocovariance Matrices
Abstract:We consider Autoregressive (AR) processes of large p, but less than n, to approximate a linear time series. Using Bartlett's formula and strong mixing conditions, we show the consistency of the large sample autocovariance matrix by banding procedure. These large sample autocovariance matrices are consistent in operator norm as long as (log p)/n goes to 0. Parameters of large AR(p) model are estimated using a regularization procedure and banding of the autocovariance matrix. We also briefly review application of banding in finding the inverse of sum of two special matrices. Real examples from physics and business are used to illustrate the proposed methods.
Dr. Wenguang Sun (USC)
Title: False Discovery Control in Large-Scale Spatial Multiple Testing
Abstract: This talk considers both point-wise and cluster-wise spatial multiple testing problems. We derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets.
Dr. Damla Senturk (UCLA)
Dr. Joseph Barr (Chief Analytics Officer, HomeUnion, Irvine, CA)
Title: Real Estate Analytics
Abstract:Real estate plays a significant part of our economy and there's no wonder that when home prices bottom out, so does the economy. The talk is about the Analytics of real estate, how location determines value, demographic dynamics, households, measuring and analyzing trends.
Dr. Ania Supady-Chavan (KeyCorp)
Title: Time Series Modeling and Forecasting an application to Banks’ stress-testing process.
Abstract: I want to invite you to participate in a small presentation on how time series modeling can be performed to establish position during simulated stress. My goal is to gain your interest in the area of challenging current modeling techniques and looking beyond standard model assumptions testing to assess the true risk of the formulated model for the intended use. I am interested in exploring the procedures that happen behind the scenes of any code’s syntax to better explore statistics that play crucial role in assessing models performance as well as the forecasting process. The forecasting of next periods ahead is the process that I would like to emphasize the most.