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Bibliografická citace

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Heidelberg ; New York : Springer, c2011
1 online zdroj (xi, 276 s.)
Externí odkaz    Plný text PDF 
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ISBN 3642162185 (e-book)
ISBN 9783642162183 (e-book)
ISBN !9783642162176 (chyb.)
Statistics and computing
Tištěná verze: Baragona, Roberto. Evolutionary statistical procedures. Heidelberg ; New York : Springer-Verlag Berlin Heidelberg, 2011 ISBN 9783642162176
Obsahuje bibliografii na s. 261-272 a rejstřík
Bio-inspired Optimization Methods -- Topics Organization -- Evolutionary Computation -- Evolutionary Computation Methods -- Properties of Genetic Algorithms -- Evolving Regression Models -- Identification -- Parameter Estimation -- Independent Component Analysis -- Time Series Linear and Nonlinear Models -- Models of Time Series -- Autoregressive Moving Average Models -- Nonlinear Models -- Design of Experiments -- Experiments and Design of Experiments
The Evolutionary Design of Experiments -- The Evolutionary Model-Based Experimental Design: The Statistical Models in the Evolution -- Outliers -- Outliers in Independent Data -- Outliers in Time Series -- Genetic Algorithms for Multiple Outlier Detection -- Cluster Analysis -- Partitioning Problem -- Genetic Clustering Algorithms -- Fuzzy Partition -- Multivariate Mixture Models Estimation by Evolutionary Computing -- Genetic Algorithms in Classification and Regression Trees Models -- Clusters of Time Series and Directional Data -- Multiobjective Genetic ClusteringReferences
This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. -- Provided by publisher.
000245721
(OCoLC)701369518

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