Complex Data Modeling and Computationally Intensive Statistical Methods

The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets, ....

The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statisticians working at the forefront of statistical analysis.

• Likelihood
• STATISTICA
• Time series
• Variance
• bayesian statistics
• biodata mining
• classification
• classification and prediction of high dimensional data
• complex data surveys
• computational methods for statistics
• data analysis
• data mining
• machine learning
• statistical methods for industry and technology
• statistics

From the reviews:

“This volume will be useful for the researchers working in this area. I read a few papers and, all in all, the book seems to have good applications. … All the papers are well structured and consistent in style and presentations. Each paper begins with an abstract and ends with a list of references. … The volume offers a host of computer intensive techniques and applications, and a number of statistical models dealing with complex and high-dimensional data-related problems.” (Technometrics, Vol. 54 (1), February, 2012)

• Ca’ Foscari University of Venice, Venice, Italy

  Pietro Mantovan

• Politecnico di Milano, Milan, Italy

  Piercesare Secchi

Pietro Mantovan has been Professor of Statistics since 1986 at the University Ca' Foscari of Venezia, Italy, where he has served as coordinator of research units, head of the Departement of Statistics, and Dean of the Faculty of Economics. He has written several articles, monographs and textbooks on classical and Bayesian methods for statistical inference. His recent research interests focus on Bayesian methods for learning and prediction, statistical perturbation models for matrix data, dynamic regression with covariate errors, parallel algorithms for system identification in dynamic models, on line monitoring and forecasting of environmental data, hydrological forecasting uncertainty assessment, and robust inference processes.

Piercesare Secchi is Professor of Statistics at MOX since 2005 and Director of the Department of Mathematics at the Politecnico di Milano. He got a Doctorate in Methodological Statistics from the University of Trento in 1992 and a PhDin Statistics from the University of Minnesota in 1995. He has written several papers on stochastic games and on Bayesian nonparametric predictive inference and bootstrap techniques. His present research interests focus on statistical methods for the exploration, classification and analysis of high dimensional data, like functional data or images generated by medical diagnostic devices or by remote sensing. He also works on models for Bayesian inference, in particular those generated by urn schemes, on response adaptive designs of experiments for clinical trials and on biodata mining. He is PI of different projects in applied statistics and coordinator of the Statistical Unit of the Aneurisk project.

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