Nonparametric Statistics And Mixture Models
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Author | : David R. Hunter |
Publisher | : World Scientific |
Total Pages | : 370 |
Release | : 2011 |
Genre | : Mathematics |
ISBN | : 9814340553 |
This festschrift includes papers authored by many collaborators, colleagues, and students of Professor Thomas P Hettmansperger, who worked in research in nonparametric statistics, rank statistics, robustness, and mixture models during a career that spanned nearly 40 years. It is a broad sample of peer-reviewed, cutting-edge research related to nonparametrics and mixture models.
Author | : J.K. Ghosh |
Publisher | : Springer Science & Business Media |
Total Pages | : 311 |
Release | : 2006-05-11 |
Genre | : Mathematics |
ISBN | : 0387226540 |
This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Author | : Geoffrey McLachlan |
Publisher | : John Wiley & Sons |
Total Pages | : 419 |
Release | : 2004-03-22 |
Genre | : Mathematics |
ISBN | : 047165406X |
An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Author | : Thomas P. Hettmansperger |
Publisher | : John Wiley & Sons |
Total Pages | : 492 |
Release | : 1998 |
Genre | : Nonparametric statistics |
ISBN | : |
Offering an alternative to traditional statistical procedures which are based on least squares fitting, the authors cover such topics as one and two sample location models, linear models, and multivariate models. Both theory and applications are examined.
Author | : Nizar Bouguila |
Publisher | : Springer |
Total Pages | : 356 |
Release | : 2019-08-13 |
Genre | : Technology & Engineering |
ISBN | : 3030238768 |
This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
Author | : David Hunter |
Publisher | : World Scientific |
Total Pages | : 370 |
Release | : 2011-01-03 |
Genre | : Mathematics |
ISBN | : 9814460966 |
This festschrift includes papers authored by many collaborators, colleagues, and students of Professor Thomas P Hettmansperger, who worked in research in nonparametric statistics, rank statistics, robustness, and mixture models during a career that spanned nearly 40 years. It is a broad sample of peer-reviewed, cutting-edge research related to nonparametrics and mixture models.
Author | : Sylvia Frühwirth-Schnatter |
Publisher | : Springer Science & Business Media |
Total Pages | : 506 |
Release | : 2006-11-24 |
Genre | : Mathematics |
ISBN | : 0387357688 |
The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.
Author | : Bruce G. Lindsay |
Publisher | : IMS |
Total Pages | : 184 |
Release | : 1995 |
Genre | : Mathematics |
ISBN | : 9780940600324 |
Author | : Dipak D. Dey |
Publisher | : Springer Science & Business Media |
Total Pages | : 376 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461217326 |
A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Author | : Michael G. Akritas |
Publisher | : Springer |
Total Pages | : 369 |
Release | : 2014-12-02 |
Genre | : Mathematics |
ISBN | : 1493905694 |
This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for Non Parametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI and other organizations. M.G. Akritas, S.N. Lahiri and D.N. Politis are the first executive committee members of ISNPS and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world. The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the world and contributes to the further development of the field. The conference program included over 250 talks, including special invited talks, plenary talks and contributed talks on all areas of nonparametric statistics. Out of these talks, some of the most pertinent ones have been refereed and developed into chapters that share both research and developments in the field.