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. Damla Senturk (UCLA)
Title: Generalized Multi-Index Varying Coefficient ModelsGeneralized Multi-Index Varying Coecient Models
Abstract:Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Although recent studies have found that the risk of cardiovascular events is higher after an infection-related hospitalization, studies have not fully elucidated how the risk of cardiovascular events changes over time for patients on dialysis. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization and (3) the patient's age at the start of dialysis. This is achieved by using a new class of generalized multiple-index varying coefficient (GM-IVC) models. The proposed GM-IVC models utilize a multiplicative structure and one-dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection-related hospitalization among the dynamic cohort of survivors. We develop a two-step estimation procedure for the GM-IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk using the United States Renal Data System database, which collects data on nearly all patients with end-stage renal disease in the U.S. Finally, simulation studies assess the performance of the proposed estimation procedures.
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. 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. 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.
Dr. Julie Swenson (UCSB)
Title: A Bayesian Approach to Recommendation Systems.
Abstract: Recommendation systems have proliferated in the last decade. Currently, most recommendation systems utilize content based algorithms, collaborative filtering based algorithms, or a combination of both. The recent surge in popularity of social networks has led to the creation of trust based algorithms. While such recommendation systems have proven that they can be more successful than their content and collaborative filtering based counterparts, they are often are plagued with problems with cold start and data sparseness. We propose a Bayesian trust based algorithm that addresses both of these problems. Our results indicate that our method can be more successful than an existing Bayesian trust based algorithm.