Evolutionary Statistical Procedures: An Evolutionary Computation Approach to Statistical Procedures Designs and Applications (Statistics and Computing) - Tapa dura

Acerca del autor

Roberto Baragona received the ‘laurea’ in Mathematics from Sapienza University of Rome, Italy, in 1972. He is currently a professor of Data Analysis at Sapienza University. His main research interests are in time series analysis and multivariate statistics with a special emphasis on meta-heuristic methods. Referee for several international journals and associate editor of Statistical Methods and Applications.

Francesco Battaglia is a professor of Statistical Forecasting at Sapienza University of Rome. He has taught at the University of Cagliari and at the Italian Public Administration School, and visited several European universities. He is a former head of Sapienza University’s Department of Statistics, and head of the Time Series Analysis Group of the Italian Statistical Society. Editor-in-chief of Statistical Methods and Applications.

Irene Poli is Professor of Statistics at Ca' Foscari University of Venice, and Director of the European Centre for Living Technology (ECLT, www.ecltech.org). Her current research involves developing statistical procedures for high dimensional data and deriving evolutionary experimental designs and multiobjective optimizations mainly for biochemical problems. She is a Fellow of the New York Academy of Science, of the Royal Statistical Society, the Bernoulli Society, and a member of the Italian Statistical Society.

De la contraportada

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.