[PDF] Statistical Modeling And Analysis For Complex Data Problems eBook

Author : Pierre Duchesne
Publisher : Springer Science & Business Media
Page : 354 pages
File Size : 34,7 MB
Release : 2005-04-12
Category : Business & Economics
ISBN : 9780387245546

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STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.

Author : William D. Dupont
Publisher : Cambridge University Press
Page : 543 pages
File Size : 22,9 MB
Release : 2009-02-12
Category : Medical
ISBN : 0521849527

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A second edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.

Author : Matteo Grigoletto
Publisher : Springer Science & Business Media
Page : 228 pages
File Size : 40,14 MB
Release : 2013-01-26
Category : Mathematics
ISBN : 884702871X

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The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.

Author : Anna Maria Paganoni
Publisher : Springer
Page : 210 pages
File Size : 23,7 MB
Release : 2014-11-04
Category : Mathematics
ISBN : 3319111493

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The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.

Author : Francesca Greselin
Publisher : Springer Nature
Page : 201 pages
File Size : 24,78 MB
Release : 2019-09-06
Category : Mathematics
ISBN : 3030211401

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This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

Author : Pietro Mantovan
Publisher : Springer Science & Business Media
Page : 170 pages
File Size : 45,94 MB
Release : 2011-01-27
Category : Computers
ISBN : 8847013860

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Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.

Author : C. P. Snow
Publisher : Cambridge University Press
Page : 193 pages
File Size : 40,99 MB
Release : 2012-03-26
Category : Philosophy
ISBN : 1107606144

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The importance of science and technology and future of education and research are just some of the subjects discussed here.

Author : Donatella Vicari
Publisher : Springer
Page : 297 pages
File Size : 46,94 MB
Release : 2014-07-05
Category : Mathematics
ISBN : 3319066927

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This volume presents theoretical developments, applications and computational methods for the analysis and modeling in behavioral and social sciences where data are usually complex to explore and investigate. The challenging proposals provide a connection between statistical methodology and the social domain with particular attention to computational issues in order to effectively address complicated data analysis problems. The papers in this volume stem from contributions initially presented at the joint international meeting JCS-CLADAG held in Anacapri (Italy) where the Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society had a stimulating scientific discussion and exchange.

Author :
Publisher : Springer
Page : 0 pages
File Size : 11,61 MB
Release : 2005-11-30
Category : Business & Economics
ISBN : 9780387261171

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In 2005, GERAD celebrates its 25th anniversary with these ten volumes covering most of the Center's research areas of expertise: Essays and Surveys in Global Optimization, edited by C. Audet, P. Hansen and G. Savard; Graph Theory and Combinatorial Optimization, edited by D. Avis, A. Hertz and O. Marcotte; Numerical Methods in Finance, edited by H. Ben-Ameur and M. Breton; Analysis, Control and Optimization of Complex Dynamic Systems, edited by E.K. Boukas and R. Malhamé; Column Generation, edited by G. Desaulniers, J. Desrosiers and M.M. Solomon; Statistical Modeling and Analysis for Complex Data Problems, edited by P. Duchesne and B. Rémillard; Performance Evaluation and Planning Methods for the Next Generation Internet, edited by A. Girard, B. Sansò, and F. Vásquez-Abad; Dynamic Games: Theory and Applications, edited by A. Haurie and G. Zaccour; Logistics Systems: Design and Optimization, edited by A. Langevin and D. Riopel; Energy and Environment, edited by R. Loulou, J.-P. Waaub and G. Zaccour.

Author : S. Ejaz Ahmed
Publisher : Springer
Page : 390 pages
File Size : 31,34 MB
Release : 2017-03-21
Category : Mathematics
ISBN : 3319415735

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This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.