Specifications
| Publisher | Springer Verlag |
| ISBN 13 | 9788847013858 |
| ISBN 10 | 8847013852 |
| Book Description | Recentyearshaveseentheadventanddevelopmentofmanydevicesabletorecordand storeaneverincreasingamountofinformation. Thefastprogressofthesetechnologies is ubiquitousthroughoutall ?elds of science and applied contexts, ranging from medicine,biologyandlifesciences,toeconomicsandindustry. Thedataprovided bytheseinstrumentshavedifferentforms:2D-3Dimagesgeneratedbydiagnostic medicalscanners,computervisionorsatelliteremotesensing,microarraydataand genesets,integratedclinicalandadministrativedatafrompublichealthdatabases, realtimemonitoringdataofabio-marker,systemcontroldatasets. Allthesedata sharethecommoncharacteristicofbeingcomplexandoftenhighlydimensional. Theanalysisofcomplexandhighlydimensionaldataposesnewchallengesto thestatisticianandrequiresthedevelopmentofnovelmodelsandtechniques,fueling manyfascinatingandfastgrowingresearchareasofmodernstatistics. |
| Editorial Review | 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) |
| About the Author | 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. |
| Language | English |
| Author | Pietro Mantovan, Piercesare Secchi |
| Publication Date | 30 Nov 2010 |
| Number of Pages | 164 |
Complex Data Modeling And Computationally Intensive Statistical Method Paperback English by Pietro Mantovan - 30 Nov 2010
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