You are invited to attend the fall meeting of the CO/WY chapter of the ASA. It will be held November 5 from 1-4pm at UC Denver Anschutz Medical Campus, room P28-2307 in the Education 2 North Building. We have several interesting topics being presented, and refreshments will be provided. We hope to see you there! Matt Ostberg President CO/WY Chapter ASA
ASA CO/WY Chapter
1:00 - 1:05 Introduction
Title: Bayesian Tracking of Emerging Epidemics Using Ensemble Optimal Statistical
Abstract: We explore the use of the ensemble optimal statistical interpolation (EnOSI) data assimilation method for the statistical tracking of emerging epidemics and to study the spatial dynamics of a disease. The epidemic models that we used for this study are spatial variants of the common susceptible-infectious-removed (S-I-R) compartmental model of epidemiology. The spatial S-I-R epidemic model is illustrated by application to simulated spatial dynamic epidemic data from the historic "Black Death" plague of 14th century Europe. Bayesian statistical tracking of emerging epidemic diseases using the EnOSI as it unfolds is illustrated for a simulated epidemic wave originating in Santa Fe, New Mexico.
Speaker: Steve Anderson
Title: Fixed effect ridge regression model for determining the mixture of subscription and acquisition cost
Abstract: The following paper presents a statistical method for analyzing the driver based expense model for Qwest Communications. It utilizes a fixed effect regression model with interaction to measure the mixture of subscription and acquisition cost by cost pool. The ratio of fixed to variable cost within each cost pool will also be determined for a proof of concept since these components arise naturally from the data. An examination of the theory will be presented prior to the discussion of the simulation results.
Speaker: Julie Roy
Abstract: For both linear and nonlinear global optimization problems, it often occurs that there are feasible lines, planes, hyperplanes, or hypersurfaces that have optimal (or approximately optimal) objective function values. For these problems, common deterministic global optimization software may only find one optimizer without even indicating that other solution points exist, and software with automatically verified complete search algorithms may not complete within a reasonable amount of time. Since there has not been much research done concerning deterministic algorithms for verified solutions to general singular global optimization problems, the purpose of this work is to propose a method for computing rigorous enclosures of sets that contain approximately feasible, approximately optimal solution points for these problems. These approximate singular solution sets are defined in terms of boxes constructed in different coordinate systems about approximately optimal points. A method for constructing approximate solution boxes is presented for problems with linear solution sets, and then the method is extended for problems with nonlinear solution sets. Additionally, some illustrative examples are provided. Future work to incorporate these methods into a branch and bound process for global optimization is discussed, and preliminary results are given.
Speaker: Ed Hess
Title: Longitudinal Parametric Regression
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