Journal article

Publication year: 2004

Pages: 211-223

Journal: Computational Statistics & Data Analysis

Volume number: 47

Issue number: 2

ISSN: 0167-9473

eISSN: 1872-7352

DOI Link: https://doi.org/10.1016/j.csda.2003.11.026

Publisher: Elsevier

Abstract: 

Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods. One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying. A different approach consists in applying optimization heuristics such as evolutionary algorithms (simulated annealing, threshold accepting), neural networks, genetic algorithms, tabu search, hybrid methods, etc., which have been developed over the last two decades. Although the use of these methods became more standard in several fields of sciences, their use in estimation and modelling in statistics appears to be still limited.A brief introduction to the computational complexity of problems encountered in the fields of statistical modelling and econometrics as well as an overview and classification of the optimization heuristics used is provided. Given the applications presented and the growing availability of optimization heuristics, it is expected that their use will become more frequent in statistics in the near future.

Harvard Citation style: Winker, P. and Gilli, M. (2004) Applications of optimization heuristics to estimation and modelling problems, Computational Statistics & Data Analysis, 47(2), pp. 211-223. https://doi.org/10.1016/j.csda.2003.11.026

APA Citation style: Winker, P., & Gilli, M. (2004). Applications of optimization heuristics to estimation and modelling problems. Computational Statistics & Data Analysis. 47(2), 211-223. https://doi.org/10.1016/j.csda.2003.11.026