Statistics Seminar – Dr. David Matteson, Cornell University: “A Spatio-Temporal Point Process Model for Ambulance Demand”

Speaker Dr. David Matteson (Cornell University)
Date            Thursday, September 18, 2014
Time            4:00 PM – 5:00 PM
Location        269 Everitt

Abstract: Ambulance demand estimation at fine time and location scales is critical for fleet management and dynamic deployment. We are motivated by the problem of estimating the spatial distribution of ambulance demand in Toronto, Canada, as it changes over discrete 2-hour intervals. This large-scale dataset is sparse at the desired temporal resolutions and exhibits location-specific serial dependence, daily and weekly seasonality. We address these challenges by introducing a novel characterization of time-varying Gaussian mixture models. We fix the mixture component distributions across all time periods to overcome data sparsity and accurately describe Toronto’s spatial structure, while representing the complex spatio-temporal dynamics through time-varying mixture weights. We constrain the mixture weights to capture weekly seasonality, and apply a conditionally autoregressive prior on the mixture weights of each component to represent location-specific short-term serial dependence and daily seasonality. While estimation may be performed using a fixed number of mixture components, we also extend to estimate the number of components using birth-and-death Markov chain Monte Carlo. The proposed model is shown to give higher statistical predictive accuracy and to reduce the error in predicting EMS operational performance by as much as two-thirds compared to a typical industry practice. This is joint work with Zhengyi Zhou, Dawn B. Woodard, Shane G. Henderson and Athanasios C. Micheas. Data was provided by Toronto EMS. This research was partially supported by NSF Grant CMMI-0926814, NSF Grant DMS-1209103 and NSF Grant DMS-1007478.

CSL – SPOTLIGHT LECTURE SERIES – “Optimal Rate Communication by Regression ”

Speaker Andrew Barron, Department of Statistics, Yale University
Date Apr 28, 2014
Time 3:00 pm
Location B 02 Coordinated Science Lab
Sponsor Coordinated Science Lab

***Abstract***
We discuss our recently developed sparse superposition codes for the Gaussian noise channel. With a fast adaptive successive decoder it achieves nearly exponentially small error probability at any fixed rate R less than the Shannon capacity. This is joint work with Antony Joseph and Sanghee Cho.

******Bio******
Professor Barron’s research interests include the areas of statistical information theory, statistical inference, model selection, probability limit theorems, asymptotics of Bayes procedures, curve and surface estimation, artificial neural networks, approximation theory, and investment theory. Received Ph.D., Electrical Engineering, Stanford University; M.S., Electrical Engineering, Stanford University; B.S. (Magna Cum Laude); E.E. and Math Science, Rice University. 1985 – 1992 Andrew was Assistant /Associate Professor of Statistics and Electrical & Computer Engineering, University of Illinois.

++++++++++++++++++++++++++

Statistics Seminar – Dr. Yongmiao Hong, Cornell University: “Autoregressive Conditional Models for Interval-Valued Time Series Data”

Speaker Yongmiao Hong (Cornell University)
Date            Thursday, April 24, 2014
Time            3:30 pm – 5:00 pm
Location        1000 Lincoln Hall

Abstract: An interval-valued observation in a time period contains more information than a point-valued observation in the same time period. Examples of interval data include the maximum and minimum temperatures in a day, the maximum and minimum GDP growth rates in a year, the maximum and minimum asset prices in a trading day, the bid and ask prices in a trading period, the long term and short term interests, and the top 10% income and bottom 10% income of a cohort in a year, etc. Interval forecasts may be of direct interest in practice, as it contains information on the range of variation and the level or trend of economic processes. More importantly, the informational advantage of interval data can be exploited for more efficient econometric estimation and inference. We propose a new class of autoregressive conditional interval (ACI) models for interval-valued time series data. A minimum distance estimation method is proposed to estimate the parameters of an ACI model, and the consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. It is shown that a two-stage minimum distance estimator is asymptotically most efficient among a class of minimum distance estimators, and it achieves the Cramer-Rao lower bound when the left and right bounds of the interval innovation process follow a bivariate normal distribution. Simulation studies show that the two-stage minimum distance estimator outperforms conditional least squares estimators based on the ranges and/or midpoints of the interval sample, as well as the conditional quasi-maximum likelihood estimator based on the bivariate left and right bound information of the interval sample. In an empirical study on asset pricing, we document that when return interval data is used, some bond market factors, particularly the default risk factor, are significant in explaining excess stock returns, even after the stock market factors are controlled in regressions. This differs from the previous …findings (e.g., Fama and French (1993)) in the literature.

+++++++++++++++++++++++++++

Statistics Seminar – Dr. Runhuan Feng, UIUC: ” A Discussion of Research Problems in Quantitative Risk Management of Variable Annuities Guaranteed Benefits”

Speaker Dr. Runhuan Feng (UIUC)
Date            Thursday April 17th, 2014
Time            4:00 pm – 5:00 pm
Location        165 Everitt

Abstract: With the increasingly fierce competition in the financial market in the past decade, the life insurance industry in North America has experienced tremendous revolutionary development with the introduction of investment guarantees. As a consequence, the quantitative risk management of investment guarantees is a relatively new territory of research that calls for new techniques that go beyond the traditional actuarial toolkit. In this talk, we will present some common types of guaranteed minimum benefits and show how we can set up mathematical models to quantify and formulate the risk management problems. This talk is intended to stimulate discussions on these research problems rather than present established results. No previous background on finance or insurance is necessary.

+++++++++++++++++++++++++++++++++++++++++