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Seminar Series: Fall 2013
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.

Tuesday, Aug 27, 2013, 2-3pm Business Building BB 3.03.20 .

  • Presenter: Dr. Hongbing Liu Associate Professor, Department of Accounting, Guangdong University of Business Studies, China

  • Presentation Title: Comprehensive Evaluation of Enterprise Financial Performance Based on Data Envelopment Analysis

  • Abstract: In extant literature of enterprise evaluation, the main approach adopted for comprehensive evaluation of enterprise financial performance is to collect multiple financial indexes, and then sum them up to get a comprehensive index. Among the many shortcomings of this approach is that every index needs to be assigned a weight coefficient subjectively, which results in relatively strong subjectivity of the evaluation result. To overcome this problem, we set up a comprehensive evaluation model of enterprise financial performance based on Data Envelopment Analysis (DEA), a well known linear programming technique. Our evaluation method also characterizes with other important features: overcoming the impossibility to carry out comprehensive ranking for multiple enterprises with comprehensive efficiency value being 1 (100%); overcoming the problem that the historical documents only carried out comprehensive evaluation of financial performance to enterprises whose profits must be positive values; allowing the flexibility to analyze the superiority and shortcoming of the financial capability of every enterprise. To test our methodology, we have applied this model to conduct a comprehensive analysis of the financial performances of 20 medical-pharmaceutical companies listed in the stock market of China in 2010.Our results provide important and interesting insights into the overall performance of the firms not available through conventional methods or standard financial records.


Tuesday, Sept. 26, 2013, 1:30-2:30pm, Business Building, Executive Conference Room BB 4.02.10.

  • Presenter: Dr. Jerry Oglesby, Director of Global Academic & Certification Programs, SAS Institute.

  • Presentation Title: : Business Analytics

  • Note: There is also an opportunity to drop by and visit with him between 9:00 and 10:00 a.m. in the Executive Conference Room. If you would like additional information about his visit, please stop by the Department of Management Science and Statistics [BB 4.01.10] or the Statistical Consulting Center [BB 4.06.02].

Tuesday, November 12, 2013, 12-1:30pm, Business Building, Executive Conference Room BB 4.02.10.

  • Presenter: Dr. Stephen Aultman and Dr. Joseph Campbell, USAA

  • Presentation Title: Big Data on USAA

  • Abstract: TBA

Thursday, December 12, 2013, 10:00 am-11:30 am, Business Building, 3.02.16.

  • Presenter: Chao Shi, Ph.D. Candidate (Dissertation Defense for Ph.D. in Applied Statistics)


  • Abstract: One of the important problems in neuroscience is determining the reliability of repeated EEG recordings over time. Quantifying the intra-individual stability/ variability in the recordings allows researchers to test for the effects of retention or learning for different types of subjects, as well as to examine the influence of external factors. In this dissertation, we consider models for R repeated EEG time series in both the time and frequency domains. We develop methodology to assess and quantify the reliability of the series. In the frequency domain, we propose three spectral closeness coefficients that quantify the closeness of the R spectra at individual frequencies and across specified frequency bands. These closeness measures are computed from the smoothed periodograms of the R EEG time series. Under certain assumptions, we derive the exact distribution of the spectral closeness coefficients scc1 and scc2 and compare their performance in terms of mean squared error. Based on the results, we propose a third closeness measure scc3 and investigate its properties across the entire frequency band [0.5, 30) Hz through a simulation study. In the time domain, we propose a sum-of-sinusoids model for the EEG time series. We develop algorithms to estimate the model parameters including the amplitudes, frequencies, phases, and the number of sinusoidal components. To compare the R series, we construct a likelihood ratio test (LRT) for testing the null hypothesis that all R series have the same parameters. The p-value of the LRT provides a measure of closeness of the R series based on the underlying parametric model and may be used to cluster the R series. We extend the single-channel model to the multi-channel case with suitable assumptions on the correlation structure in the spatial and time domains. We illustrate the performance of the proposed methods on two real EEG datasets. The results based on the spectral closeness coefficients show spectral features of high reliability that may be used as an input for a brain-computer interfacing (BCI) system. Using the LRT based on the sum-of-sinusoids model, we can determine which of the R series on a single subject are similar, and also quantify the differences between subjects.

  • Supervising Professor: Nandini Kannan, Ph.D.


Tentative Date - Fall 2013, 2-3pm, Business Building [This seminar will be scheduled in Spring 2014 semester.]

  • Presenter: Mike Daniels, Professor, Section of Integrative Biology, Division of Statistics & Scientific Computation, The University of Texas at Austin

  • Presentation Title: TBA

  • Abstract: TBA



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