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Seminar Series: Spring 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, Jan. 20, 2012, 10:30-11:50am, Business Building 4.02.10 (Executive Conference Room), joint seminar with ASA-San Antonio Chapter.

  • Presenter: Robert McCulloch, Professor of Information, Risk & Operations Management Department, McCombs School of Business, University of Texas at Austin

  • Joint Work with Hugh Chipman (Acadia), Ed George (U Pennsylvania),
    James Gattiker, Dave Higdon, Matt Pratola, Los Alamos National Laboratories
  • Presentation Title: Bayesian Additive Regression Trees and Parallel Computation

  • Abstract: In BART: Bayesian Additive Regression Trees, we developed a general approach for fitting the model y = f(x) + e with minimal assumptions about the structure of f. The function f is modeled as a sum of simple tree models.  This sum is made meaningful by a Bayesian analysis with a simple yet powerful prior specification that provides for both overall regularization of the fit and the limiting of the contribution of each individual tree to the sum.   An MCMC algorithm provides inference. The BART model is shown to have excellent out of sample predictive performance.
    In this talk we review BART and report results on an implementation using MPI (Message Passing Interface) for parallel computation. For problems with large sample sizes,  the algorithm is k times faster when k processors are used.

 

Friday, Feb. 10, 2012, 12noon-1pm, Business Building 4.02.10 (Executive Conference Room).

  • Presenter: Joel Edmund Michalek, PhD Professor and Vice Chairman, Department of Epidemiology & Biostatistics University of Texas Health Science Center at San Antonio

  • Co-authors: Ken Ouyang MS, and Chris Louden MS
  • Presentation Title: A Fourier and Wavelet Analysis of Serial Glucose Measurements
  • Abstract: Normoglycemia maintained by tight glucose control protocols are reported to reduce morbidity and mortality in critically ill surgical patients. Glucose variability has been used to characterize glycemic control and efficacy of tight glucose control protocols; however, the definition and methods for determining glucose variability have not been standardized. This was a single-center retrospective review of adult trauma patients admitted to the Surgical Trauma Intensive Care Unit at a Level I Trauma Center between January 2005 and December 2007. Patients were managed with a tight glucose control protocol with target range 80-110mg/dl. Subjects were identified by trauma registry query and glucose values were obtained through the hospital database. Patients with an intensive care unit length of stay <3 days and those with mild injury severity were excluded. Glucose variability was calculated using glucose measurements made during the first 3 days using two measures of glucose curve complexity, Fourier power and Wavelet energy. The primary endpoint was death between day 3 and day 30. Logistic regression and Cox models were used to assess the significance of associations between complexity and death and between complexity and time to death. Associations found were significant but not predictive.

     

Friday, March 23rd, 2012, 2-3pm, Business Building 4.02.10 (Executive Conference Room).

  • Presenter: Long Liu, PhD, Assistant Professor, Department of Economics, University of Texas at San Antonio

  • Presentation Title: The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term

  • Abstract: This paper considers the estimation of a linear regression involving the spatial autoregressive (SAR) error term which is nearly nonstationary. The asymptotics properties of the ordinary least squares (OLS), true generalized least squares (GLS) and feasible generalized least squares (FGLS) estimators as well as the corresponding Wald test statistics are derived. The limiting behavior of spatial regression diagnostics such as Moran I and Lagrange Multiplier (LM) statistics are also analyzed. Monte Carlo results are conducted to study the sampling behavior of the proposed estimators and test statistics.

 

Monday, April 10, 2012. 10:00-11:30 am, Stat Lab (BB 3.02.16).

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

  • Presentation Title: COMBINING DIVERSE CLASSIFIERS USING CLASS SPECIFIC PRECISION INDEX FUNCTION

  • Abstract: In this work the overall precision index (PIN) (Ko and Windle 2011) is extended to a class-specific precision index function (PIC) and used to combine diverse classifiers. The PIN is a measure of overall local prediction accuracy for a classifier, whereas the PIC is a measure of local class prediction accuracy. The main motivation for extending the precision index function to be class-specific is to generate higher precision prediction sets than can be used to identify potential candidates such as when classifying proteins to sub-cellular location sites. This work compares the performances of PIC and PIN to other combining methods including majority voting, stacked generalization, and cluster-selection for the well-known data sets: 1) vowel recognition data (Hastie, Tibshirani et al. 2009) which is a balanced data set, and 2) yeast protein localization data (Frank 2010) which is an unbalanced data set. When comparing the PIC method to other combining methods for the vowel recognition data, the PIC method was not able to generate high precision prediction sets. Similar results were obtained with an extension of the static cluster-selection method to the class-specific level. Modified PIC curves were generated which used the two classes predicted by a classifier with the highest posterior probabilities for each prediction point. This enhancement increased the overall precision of the PIC method and extended the results to higher precisions for the vowel recognition data. A new weighted precision index from PIC and PIN was also developed which further extended the PIC results to higher precisions. The weighted precision index method outperformed most combining methods and slightly outperformed the PIN method at higher precisions. Even though the PIC method was outperformed by other methods at lower precisions for the yeast localization data, it generated higher recalls in the high precision range. The class-specific cluster selection extension, considered in this work, outperformed all other combining methods for the yeast localization data set demonstrating great potential for this method to leverage class-specific performance. The overall precision results obtained with the yeast protein localization data set for both precision index methods and cluster-selection methods outperformed previous results reported by Chen (Chen 2010) where several classifying methods including: decision trees, neural networks, naives Bayes, and Bayesian model averaging methods were considered.

Friday, April 20, 2012, 10:00-11:00am, Business Building 4.02.10 (Executive Conference Room).

  • Presenter: Angel Díaz Matalobos, PhD, Professor of Operations and Director of PhD Program, Instituto de Empresa Business School, María de Molina 12 bajo, Madrid 28006, Spain

  • Presentation Title: The Roles of Logistics Inventory and IT Practices – an European Perspective

  • Abstract: The importance of inventories cannot be overstated. In USA, private inventories alone represented over US$ 2,200 billion in 2008, more than 15% of the GDP. From an IT implementation perspective, inventory management is also highly relevant. For example, Reuters (2008) reports that one of the most commonly implemented ERP modules in companies is the materials management module, which is the module used for the management of purchasing and inventory.
    While the optimization of inventories has been the object of extensive academic interest from a modeling perspective (Kouvelis et al. 2005, Gupta et al. 2006), managerial inventory practices have been much less researched in empirical settings. In this paper we explore the issue by extracting constructs that operationalize inventory practices from a survey data that was collected to measure logistics and supply chain competitiveness in Spain.
    Inventory Practices can reduce organizational complexity by providing focus and pooling risks, facilitating the synchronization of inventory flow in the supply chain, and reducing uncertainty. As such, inventory practices address the classical problem of noise amplification found in supply chains, more commonly referred to as the bullwhip effect (Sterman 1989).
    A first group of Inventory Practices that aim to provide focus and risk pooling include the systematic codification and classification of items and the use of multi-site inventories. These are more specifically: i) Codification Practices, a systematic approach to coding and equipment standardization; ii) Classification Practices, of which the best known is the ABC classification practice, used to provide focus on important items; and iii) The simultaneous use of multiple stock keeping sites in inventory management, or multi-echelon practices, which allows for both the centralization of stock (enabling risk pooling) and decentralization (increasing service quality).
    A second group of practices that facilitate continuous flows and synchronization in the supply chain include: i) The use of just-in-time and continuous replenishment policies; ii) the use of modularization and postponement strategies;  and iii) the use of vendor-managed inventory (VMI) and consignation stock practices.  More specifically Just-in-time practices, which are extensively covered in the literature (Alles et al. 2000, Schwarz and Weng 2000), facilitate a reduction in lot sizes and a more continuous and synchronized flow of goods, thanks to shorter production set-up times and lower purchasing transaction costs; Modularization and postponement practices, referring to practices aimed at reducing complexity in inventory flows, facilitate risk-pooling effects upstream and allow the configuration of the final product to be defined closer to, or by the customer, facilitating a make-to-order approach leading to a reduction of inventory and forecasting errors and an improvement of companies’ cash-flow (Boone et al., 2007, Sánchez-Rodríguez et al. 2008). VMI and consignment stocks, collaborative inventory practices in the supplier-customer dyad, further facilitate the synchronization of flows (Gümü et al, 2008.)
    Due to the modeling distortions caused by unpredictable demand variability, and to the smoothing improvements brought by forecasting, there has been a long association between demand management and forecasting and inventory management (Syntetos et al. 2009, Díaz et al. 2006).
    Finally, the impact of information technology on inventory management too has been widely studied (Ketzenberg et al. 2007, Díaz 2003, Díaz et al. 2006). One critical way in which information seems to create value in inventory management is through a reduction in uncertainty. This is particularly important in inventory management where repetitive transactions and processes are carried out for large quantities of stock keeping units. 

    The presentation is organized as follow: we first present some details of the benchmarking analysis of logistics practices performed for the ministry in Spain. We the describe in more detail the components of the Inventory Practices construct. We finally show results from two articles where the construct is used to evaluate Supply Chain Integration.

     

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Thursday, August 23rd, 2012, 10-11am Business Building BB 3.03.14

  • Presenter: Kai Li, PhD, Former Dean of Management School, Professor of Operations Management, and Director of Institute for Industrial Economics and Management, Northeastern University, China.

  • Presentation Title: Countervailing Power and Resale Price Maintenance

  • Abstract: Resale price maintenance (RPM) is a common way for upstream manufacturers to control downstream retailers in vertical relations like in a supply chain, while countervailing power is the ability of a retailer to break away from the constraint set by the upstream manufacturer. In traditional economic theory, RPM should be implemented in such vertical relations in which the downstream is composed only of small retailers without much negotiation ability against the upstream manufacturer. The possibility of the coexistence of RPM and countervailing power is analyzed by introducing countervailing power into the RPM structure. Primary results show that even the dominant retailer with countervailing power has the incentive to reach a RPM agreement with a monopoly manufacturer. This means there is a mechanism for the coexistence of RPM and countervailing power and this mechanism can be used to explain the complicated phenomena in vertical relations.

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