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Seminar Series: FALL 2009
The seminar series will usually take place on Fridays in a Business Building room, but the exact time and location could be different due to a variety of factors including room availability in the Business Building. For actual direction to the Business Building, please see campus map. For additional information contact Dr. Kefeng Xu, (210) 458-5388.

Friday, Oct.16, 2009 (Time and Location: 2-3pm, Executive Conference Room BB 4.02.10)

  • Presenter: Brad H. Pollock, MPH, PhD, Professor and Chairman, Department of Epidemiology & Biostatistics University of Texas Health Science Center at San Antonio

  • Presentation Title: A Randomized Controlled Intervention Trial to Improve Treatment Adherence for Adolescents and Young Adults with Cancer

  • Abstract: Cancer is the leading cause of death for adolescents and young adults (AYAs). This age group experiences lower survival relative to younger patients. There are a number of potential factors that account for age-related outcome differences. One of these includes poor adherence to prescribed cancer therapy. We conducted a large multi-site randomized intervention trial of a videogame designed to improve treatment adherence and other health-related behaviors in AYAs undergoing active cancer treatment. The results from this trial as well as implications for AYA cancer research will be discussed.

Friday, Oct. 30, 2009 (Time and Location: 12:30-1:30pm, Executive Conference Room BB 4.02.10)

  • Presenter: Dr. Daijin Ko, Professor, Department of Management Science and Statistics, College of Business, University of Texas at San Antonio

  • Presentation Title: An Introduction to Neurostatistics

  • Abstract: Neurostatistics/Neuroinformatics is a multidisciplinary subject dealing with the analysis and management of data gathered from the nervous system. The data are digitally acquired, stored and analyzed in large volumes. The disciplines relevant to Neurostatistics/Neuroinformatics include neuroscience, statistics, probability/mathematics, signal processing, computer science, and the physical sciences.

    A dominant format of data gathered in this field is time series. It includes invasively measured electrophysiological data (single and multiple spike trains), non-invasively measured electrophysiological data (local field potential, EEG/MEG recordings) and time series data obtained using optical or magnetic resonance imaging (fMRI) or PET. Other time series data types include behavioral measurements. Statistically, the setting involves both continuous multiple time series (time-varying stimuli and time-varying neuronal responses) and inhomogeneous point processes, sometimes hundreds of them observed simultaneously.

    In this talk, I give an example of statistical analysis of an electrophysiological point process data, the spike train data. I review and evaluate useful strategies for identifying an important pattern of the point process, the bursts and pauses and propose a new statistical method. It is used in testing a hypothesis generated by neuroscientists at UTSA that underlying the mechanism by which bursts are generated involves the activation of NMDA receptors on dopaminergic neurons.

Friday, Nov. 6, 2009 (Time and Location: 2-3pm, Executive Conference Room BB 4.02.10)

  • Presenter: Hon Keung Tony Ng, Associate Professor, Department of Statistical Science, Southern Methodist University

  • Presentation Title: Statistical Analysis of Adaptive Progressively Censored Data

  • Abstract: In this talk, I will introduce a mixture of Type-I censoring and Type-II progressively censoring schemes, called adaptive progressively hybrid censoring scheme, which is useful in life-testing or reliability experiments. This censoring scheme can be viewed as a design in which we are assured of getting a pre-fixed number of observed failure times for efficiency of statistical inference plus the total time on test will not be too far away from an ideal time limit. Parameter estimation for the exponential and extreme value distributions is discussed. Different estimation methods are compared using Monte Carlo simulation. Joint work with D. Kundu (Indian Institute of Technology Kanpur) and P. S. Chan (Chinese University of Hong Kong).

Friday, Nov. 20, 2009 (Time and Location: 2-3pm, Executive Conference Room BB 4.02.10)

  • Presenter: Dr. Don Lien, Professor, Department of Economics, College of Business, University of Texas at San Antonio

  • Presentation Title: Statistical Methods in Inventory Effect and Analysis

  • Abstract: This chapter examines several statistical methods applied to analyze a conjecture provided by Houthakker (1959, 1968) concerning the relationship between the inventory of a commodity and the correlation between spot and futures prices of the commodity. This relationship is termed inventory effect in Fort and Quirk (1988) and presents an argument to support backwardation in futures markets.

Specifically, the inventory effect suggests that the correlation between spot and futures prices is greater when the inventory is large (and correspondingly the spot price is low) compared to when the inventory is small (and correspondingly the spot price is high). To analyze this conjecture, we need first to characterize the joint distribution between spot and futures prices. This chapter summarizes four approaches currently available in the literature: bivariate normal distributions, bivariate log-normal distributions, ordered bivariate normal distributions, and ordered bivariate log-normal distributions. These approaches generate different conclusions regarding the validity of the inventory effect, indicating the result is not robust to the distribution assumption. Therefore, the inventory effect may prevail in some commodity markets but not in others. Using available price data to evaluate possible distribution specifications is required to determine the existence of the inventory effect in a futures market.

Friday, Dec. 4, 2009 (Time and Location: 2-3pm, BB 3.01.06)

  • Presenter: Dr. Dennis Lin, University Distinguished Professor of Supply Chain and Statistics, Penn State University

  • Presentation Title: Business, Industry and Government (BIG) Statistics

  • Abstract: In the past decades, we have witnessed the revolution of information technology. Its impact to statistical research is enormous. This talk attempts to address recent developments and some potential research issues in
    Business, Industry and Government (BIG) Statistics, with special focus on computer experiment and information systems. An overall introduction and review will be given, followed by specific research potentials. For each subject, the problem will be introduced, some initial results will be presented, and future research problems will be suggested.
    If time permits, I will also discuss some recent advances in Search Engine and RFID study.
    Slides of his talk can be downloaded at the website
  • Bio: Dr. Dennis Lin is a University Distinguished Professor of Supply Chain and Statistics at Penn State University.
    His research interests are quality engineering, industrial statistics, data mining and response surface. He has published over 150 papers in a wide variety of journals. He currently serves as co-editor for Applied Stochastic Models for Business and Industry, and associate editor for various journals: Technometrics, Statistica Sinica, Journal of Quality Technology, Journal of Data Science, Quality Technology & Quality Management, Journal of Statistics and Its Applications, and Journal of Statistical Theory and Practice. Dr. Lin is an elected fellow of ASA and ASQ,
    an elected member of ISI, a lifetime member of ICSA, and a fellow of RSS. He is an honorary chair professor for various universities, including a Chang-Jiang Scholar of China at Renmin University, National Chengchi University (Taiwan), Fudan University, and XiAn Statistical Institute (China). He is also the recipient of the 2004 Faculty Scholar Medal Award at Penn State University.

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