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Seminar Series: Fall 2012
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. 12, 2012, 2-3pm, Business Building 4.02.10 (Executive Conference Room). joint seminar with ASA-San Antonio Chapter.

  • Presenter: Eddy Kwessi, Assistant Professor, Mathematics Department, Trinity University

  • Presentation Title: Efficient Rank Regression with Wavelet Estimated Scores

  • Abstract: An Asymptotically efficient rank estimation of nonparametric linear models is proposed. The approach is based on the estimation of the score function in the rank dispersion function using compactly supported wavelets. As a by- product of this approach, we obtain a consistent estimator of the asymptotic variance of the rank estimator. We will discuss extensions of this result to mixed effect models.

 

Friday, Nov. 2, 2012, 2-3pm, Business Building 3.03.02.

  • Presenter: Krystel Castillo (Speaker's Bio here), Ph.D. Assistant Professor, Mechanical Engineering (Manufacturing), The University of Texas at San Antonio

  • Presentation Title: ENHANCING PERFORMANCE THROUGH STATISTICAL ANALYSIS: A CASE STUDY IN THE GLASS INDUSTRY

  • Abstract: Quality improvement initiatives have become an important factor to be considered for engineering managers and an important part of any strategic plan in organizations. A study was conducted at a glass container company located in Mexico which is in the beginning stage of implementing Six Sigma projects. The study was oriented to control the bottom temperature, through statistical analysis, of a recuperative furnace designated for the production of bottles. The objective was to meet the quality specification given as a bottom temperature in the fining zone (BTFZ). The engineers faced the problem of not knowing the percentage of recycled glass that has the best performance, not knowing the impact of key factors, and not having a predictive model for the BTFZ. A statistical approach is presented to investigate the effect that different levels of recycled glass have on the overall quality. This talk also presents a methodology to control the BTFZ.

 

Friday, Nov. 30, 2012, 2-3pm, Business Building 3.03.02. Joint seminar with ASA-San Antonio Chapter.

  • Presenter: Javier Rojo, Professor of Statistics, Department of Statistics, Rice University, Houston, Texas

  • Presentation Title: Dealing with high-dimensionality in Survival Analysis

  • Abstract: As a consequence of modern high-throughput data, the need to reduce dimension has been exacerbated. Classical methods such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) have been used in the literature to achieve the reduction of dimension. In the context of survival analysis, the issue becomes more challenging as one needs to take into account the censoring mechanism. In this talk we revisit the classical methods and compare them in their ability to accurately estimate the survival function and the coefficients of the covariates in the model. We present a new way of doing PLS reduction that is more robust in the presence of outliers both in the survival times and the covariates. Finally, we explore the potential contribution of random projections as a dimension reduction tool.

 

 

Thursday, December 6, 2012 10:00 am -11:30 am BB 3.02.16.

  • Presenter: Bin Chen, Ph.D. Candidate, Ph. D. APPLIED STATISTICS DISSERTATION DEFENSE, Department of Management Science & Statistics, University of Texas at San Antonio

  • Presentation Title: BAYESIAN MODEL SELECTION IN FINITE MIXTURE REGRESSION

  • Abstract: The finite mixture regression is a common method to account for heterogeneity in relationship between the response variable and the covariates. One of the goals of this study is variable selection within each component in a finite mixture regression. This has not been studied in the literature from Bayesian perspective. We propose an approach by embedding variable selection into the data augmentation method that iteratively updates estimation in two steps: estimate parameters for each component and determine the latent membership for each observation. Componentwise variable selection is realized by imposing special priors or procedures designed for parsimony in the first step. Due to separation of the two steps, our approach provides the freedom to choose from a wide variety of variable selection techniques. A simulation study is conducted to assess performance of the proposed approach under a variety of scenarios through investigating accuracy of variable selection and clustering. Simulation studies show that the proposed approach successfully identifies important variables even in the noisy scenarios. When the approach is applied to real datasets, we find selected variables are quite different between components, which provide additional insight to scientific understanding. The other goal of this dissertation is to determine the number of components when it is unknown a priori, which is another model selection issue in finite mixture models. We propose a reductive procedure with a new distance measure. This measure is based on the posterior predictive replicates called “SWAP”. This is different from commonly-used posterior predictive replicates in the sense that SWAP uses parameters from other components. At each MCMC iteration, the measure is utilized to judge whether there exist two components close enough to be collapsed. The posterior probability of the collapsing will further indicate whether there is strong evidence in reducing the number of components. The results from the simulation study show that the proposed method performs well and is more stable than AIC and BIC. When applied to real data, the method gives a reasonable estimate and is more in line with AIC than other criteria.

  • Supervising Professor: Keying Ye, Ph.D.

 

Tuesday, December 11, 2012 2:00 pm -3:30 pm BB 3.02.16.

  • Presenter: Yi Cao, Ph.D. Candidate, Ph. D. APPLIED STATISTICS DISSERTATION DEFENSE, Department of Management Science & Statistics, University of Texas at San Antonio

  • Presentation Title: BAYESIAN ANALYSIS OF COUNT DATA AND ITS APPLICATION IN DEMOGRAPHY

  • Abstract: Count-data modeling is often used in statistical applications, including demography. For example, annual counts of births and population in each county provide us valuable information in estimating age-specific fertility rate (ASFR) and fertility pattern which are two important subjects to demography researchers and policymakers. However, common issues encountered in modeling and estimating the count data are excessive zeros than expected by the standard Poisson model and large variabilities in parameter estimates caused by small sample sizes. Failure to account the zero inflation and small sample size will cause large bias and variance in estimation. In this dissertation, we propose a zero-inflated Poisson model with scaled-skewed normal function (ZIP-SSN) as the trajectory of Poisson rates to estimate fertility pattern while accommodates excessive zeros in count data. To tackle the complexity of posterior sampling in parameter estimation, Markov chain Monte Carlo (MCMC) algorithms including the Metropolis-Hastings and the Gibbs sampling are implemented to sample the posterior distributions of parameters and construct associated Bayesian credible regions. Meanwhile, to estimate the ASFR for an individual county, we extend the Bayes hierarchical technique to the Bayes benchmarking approach in the sense that the aggregated estimator matches with the direct estimator of a larger geographical territory. Such overall agreement with larger territory is important when modeler needs to convince policymakers the utility of the model. A study on the fertility rate of 254 counties in Texas is carried out to demonstrate the utility of ZIP-SSN model in estimating ASFR pattern at larger geographical territory. The same data set also is used to demonstrate the superiority of Bayes benchmarking approach in total fertility rate estimation for an individual county of Texas.

  • Supervising Professors: Jerome Keating, Ph.D., and Keying Ye, Ph.D.

 

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