Evolutionary Statistical Procedures [electronic resource] : An Evolutionary Computation Approach to Statistical Procedures Designs and Applications / by Roberto Baragona, Francesco Battaglia, Irene Poli.
By: Baragona, Roberto [author.].
Contributor(s): Battaglia, Francesco [author.] | Poli, Irene [author.] | SpringerLink (Online service).
Material type:
BookSeries: Statistics and Computing: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XII, 276 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642162183.Subject(s): Statistics | Medical laboratories | Computer vision | Algorithms | Mathematical statistics | Social sciences -- Methodology | Statistics | Statistics and Computing/Statistics Programs | Computer Imaging, Vision, Pattern Recognition and Graphics | Algorithms | Laboratory Medicine | Methodology of the Social SciencesDDC classification: 519.5 Online resources: Click here to access online Introduction -- Evolutionary Computation -- Evolving Regression Models -- Time Series Linear and Nonlinear Models -- Design of Experiments -- Outliers -- Cluster Analysis.
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. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.
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