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Seminar Series: Spring 2015
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, Feb. 6, 2015, 9-10:15am, Business Building 4.02.10 (Executive Conference Room)

  • Presenter: Dr. Pang Du, Associate Professor, Department of Statistics, Virginia Tech

  • Presentation Title: Cure Rate Models with Nonparametric Forms of Covariate Effects

  • Abstract: In some survival analysis of medical studies, there are often long-term survivors who can be considered as permanently cured. The goals in these studies are to estimate the non-cured probability of the whole population and the hazard rate of the susceptible subpopulation. When covariates are present as often happens in practice, modeling covariate effects on the non-cured probability and hazard rate is of equal importance. The existing methods are limited in their restrictive parametric forms of covariate effects. In this talk, we will look at several models with nonparametric forms of covariate effects that provide the extra flexibility. Smoothing spline modeling of these effects are developed. Our work consists of three pieces generalizing the promotion cure rate model and two different settings of the two-component mixture cure rate model.

 

Friday, Feb. 13, 2015, 8:45-10am, Business Building 4.02.10 (Executive Conference Room)

  • Presenter: Dr. Samiran Sinha, Associate Professor, Department of Statistics, Texas A&M University

  • Presentation Title: Semiparametric Bayesian Analysis of Censored Linear Regression with Errors-in-Covariates

  • Abstract: Accelerated failure time (AFT) model is a well known alternative to the Cox proportional hazard model for analyzing time-to-event data. In this paper we consider fitting an AFT model to right censored data when a predictor variable is subject to measurement errors. First, without measurement errors, estimation of the model parameters in the AFT model is a challenging task due to the presence of censoring, especially when no specific assumption is made regarding the distribution of the logarithm of the time-to-event. The model complexity increases when a predictor is measured with error. We propose a nonparametric Bayesian method for analyzing such data. The novel component of our approach is to model 1) the distribution of the time-to-event, 2) the distribution of the unobserved true predictor, and 3) the distribution of the measurement errors all nonparametrically using mixtures of the Dirichlet process priors. Along with the parameter estimation we also prescribe how to estimate survival probabilities of the time-to-event. Some operating characteristics of the proposed approach are judged via finite sample simulation studies. We illustrate the proposed method by analyzing a data set from an AIDS clinical trial study.

 

Friday, March 6, 2015, 2-3pm, Business Building 4.02.10 (Executive Conference Room) - Joint Seminar with ASA San Antonio Chapter

  • Presenter: Dr. Harrison (Skip) Weed

  • Presentation Title: Statistical Methods in Industry – A Personal Perspective on the Past and Future Relevance

  • Abstract: Based on over 50 years of teaching and consulting experience in the application of statistical methods in both university and industrial environments, we examine how the use, the role and the acceptance of statistical methods has changed over this time and raise some questions about its future role in the fast changing business environment of the 21st century. The challenge still exists to determine what data to collect, how much to collect, and how to extract the most information from that data in an efficient manner. But the growth of Information Technology (IT) and in particular the evolution of computer power and accessibility has brought into question the current relevance of classical statistical methods in both their application and the training in those methods.

 

 

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