4 présentations

• 
  15h30 - 15h55

  Efficiency Evaluation with Imprecise Data - An application to the Spanish Textile Industry

  • Vicente Coll-Serrano, prés., University of Valencia
  • Ismael Baeza-Sampere, University of Valencia

  Efficiency results obtained by applying conventional DEA models are usually used for Decision Making. It is common to rank the assessed units and to show the percentages of improvement that should be promoted. However, these assessments are based on the assumption of certain data and, as noted by some authors in the academic literature, this assumption is not always acceptable. In these cases, and when we search the robustness of our statements, it is preferable to consider some kind of imprecision in the data. In this study we propose a possibilistic model, which we apply to the Spanish textile firms.

• 
  15h55 - 16h20

  GARCH Parameter Estimation using Chebyshev Polynomials

  • Javier de Frutos, GERAD - Universidad de Valladolid
  • Víctor Gatón Bustillo, prés., University of Valladolid

  The objective of the paper is to calibrate the parameters in a GARCH model from real data. The usual approach is to minimize a mean-square dollar objective function as in Duan and Zhang (2009), Christoffersen (2004) among others. We propose a method (which is applicable to a general GARCH model) based on Chebyshev interpolation. In a first step, after choosing a particular GARCH specification, we build a matrix of contract prices calculated with enough precision for different parameters values including initial volatility and risk-free interest rate. This has to be done just once. Afterwards, Least-Squares estimation is carried out using Chebyshev interpolated values of the objective and Jacobian functions. The method is fast and performs the calibration in a fraction of the time that a direct implementation needs.

• 
  16h20 - 16h45

  Nonsmooth Nonconvex Approach to Bilevel Programs in Machine Learning

  • James Blondin, prés., Rensselaer Polytechnic Institute
  • Kristin P. Bennett, Rensselaer Polytechnic Institute

  We develop a nonsmooth nonconvex optimization approach for solving bilevel programs resulting from support vector machine model selection problems. By expressing lower level problems as penalized nonsmooth nonconvex constraints, the problem can be optimized using a decomposable Lagrangian method. The proposed approach is more efficient than prior bilevel approaches.

• 
  16h45 - 17h10

  Inexact Sample Average Approximation for the Stochastic ConFL Problem

  • Maria Bardossy, prés., University of Baltimore
  • S. (Raghu) Raghavan, University of Maryland

  We consider the ConFL problem where there is uncertainty on the assignment costs, and propose an inexact sample average approximation (SAA) approach. This inexact SAA relies on a heuristic to solve the sample problems yet yields tight confidence intervals at a fraction of the time required by the standard SAA approach.