Many problems in statistics and econometrics offer themselves naturally to the use of optimization heuristics. Standard methods applied to highly complex problems oftenproduce approximate results, of unknown quality, based on heavy assumptions.Optimization heuristic methods provide powerful results to many complex problems, combined with relatively simple implementation.• Offers a self-contained introduction to optimization heuristics in econometrics and statistics• Features many examples of optimization heuristic methods applied to real problems• Includes detailed coverage of the threshold accepting heuristic• Provides suggestions for further readingSplit into three parts, the book opens with a general introduction to optimization in statistics and econometrics, followed by detailed discussion of a relatively new and very powerful optimization heuristic, threshold accepting. The final part consists of many applications of the methods described earlier, encompassing experimental design, model selection, aggregation of time series, and censored quantile regression models.Those researching and working in econometrics, statistics and operations research are given the tools to apply optimization heuristic methods to real problems in their work. Postgraduate students of statistics and econometrics will find the book provides a good introduction to optimization heuristic methods.
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[(Optimization Heuristics in Econometrics and Statistics: Applications Using Threshold Accepting )] [Author: Peter Winker] [Jan-2001]: Peter Winker: Books - Amazon.ca
We report computational results in the cases where the benchmarks are market indices tracked by a small number of assets. We find that the threshold accepting heuristic is an efficient optimization technique for this problem.