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.

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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.

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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.

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Statistics Seminar – Dr. Yanqing Sun, University of North Carolina at Charlotte: ” Semiparametric Additive Hazards Regression Models for Case-Cohort/Two-Phase Sampling Designs ”

Speaker Dr. Yanqing Sun (University of North Carolina at Charlotte)
Date            Thursday, April 03, 2014
Time            4:00 PM – 5:00 PM
Location        165 Everitt

Abstract: Under the case-cohort design introduced by Prentice (1986), the covariate histories are ascertained only for the subjects who experience the event of interest (i.e., the cases) during the follow-up period and for a relatively small random sample from the original cohort (i.e., the subcohort). The case-cohort design has been widely used in clinical and epidemiological studies to assess the effects of covariates on failure times. Most statistical methods developed for the case-cohort design use the proportional hazards model, and few methods allow for time-varying regression coefficients. In addition, most methods disregard data from subjects outside of the subcohort, which can result in inefficient inference. Addressing these issues, this paper proposes an estimation procedure for the semiparametric additive hazards model with case-cohort/two-phase sampling data, which allows the effects of some covariates to be time varying while specifying the effects of others to be constant. An augmented inverse probability weighted estimation procedure is proposed, which is more efficient than the widely adopted inverse probability weighted complete-case estimation method. The asymptotic properties of the proposed estimators are established, and the finite-sample properties are examined through an extensive simulation study. The method is applied to analyze data from a preventive HIV vaccine efficacy trial. This is a joint work with Xiyuan Qian, Qiong Shou, and Peter Gilbert.
Wishing you a lovely day,