Statistical Inference Under Mixture Models
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Author | : Jiahua Chen |
Publisher | : Springer Nature |
Total Pages | : 330 |
Release | : 2023-12-24 |
Genre | : Mathematics |
ISBN | : 9819961416 |
Download Statistical Inference Under Mixture Models Book in PDF, Epub and Kindle
This book puts its weight on theoretical issues related to finite mixture models. It shows that a good applicant, is an applicant who understands the issues behind each statistical method. This book is intended for applicants whose interests include some understanding of the procedures they are using, while they do not have to read the technical derivations. At the same time, many researchers find most theories and techniques necessary for the development of various statistical methods, without chasing after one set of research papers, after another. Even though the book emphasizes the theory, it provides accessible numerical tools for data analysis. Readers with strength in developing statistical software, may find it useful.
Author | : Daniel Meddings |
Publisher | : |
Total Pages | : 0 |
Release | : 2014 |
Genre | : |
ISBN | : |
Download Statistical Inference in Mixture Models with Random Effects Book in PDF, Epub and Kindle
Author | : |
Publisher | : |
Total Pages | : 356 |
Release | : 2014 |
Genre | : |
ISBN | : |
Download Statistical Inference in Mixture Models with Random Effects Book in PDF, Epub and Kindle
Author | : Geoffrey McLachlan |
Publisher | : John Wiley & Sons |
Total Pages | : 419 |
Release | : 2004-03-22 |
Genre | : Mathematics |
ISBN | : 047165406X |
Download Finite Mixture Models Book in PDF, Epub and Kindle
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 | : 屠烈偉 |
Publisher | : |
Total Pages | : |
Release | : 2017-01-26 |
Genre | : |
ISBN | : 9781361132975 |
Download Statistical Inference on a Mixture Model Book in PDF, Epub and Kindle
Author | : Lit-wai Tao |
Publisher | : |
Total Pages | : 136 |
Release | : 1993 |
Genre | : Mixture distributions (Probability theory) |
ISBN | : |
Download Statistical Inference on a Mixture Model Book in PDF, Epub and Kindle
Author | : Nizar Bouguila |
Publisher | : Springer |
Total Pages | : 355 |
Release | : 2019-08-13 |
Genre | : Technology & Engineering |
ISBN | : 3030238768 |
Download Mixture Models and Applications Book in PDF, Epub and Kindle
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 R. Hunter |
Publisher | : World Scientific |
Total Pages | : 370 |
Release | : 2011 |
Genre | : Mathematics |
ISBN | : 9814340553 |
Download Nonparametric Statistics and Mixture Models Book in PDF, Epub and Kindle
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 | : Yuejiao Fu |
Publisher | : Library and Archives Canada = Bibliothèque et Archives Canada |
Total Pages | : 276 |
Release | : 2004 |
Genre | : |
ISBN | : 9780612945746 |
Download Statistical Inference for Mixture Models [microform] Book in PDF, Epub and Kindle
Author | : Vijay Nair |
Publisher | : World Scientific |
Total Pages | : 698 |
Release | : 2007 |
Genre | : Mathematics |
ISBN | : 9812708294 |
Download Advances in Statistical Modeling and Inference Book in PDF, Epub and Kindle
There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies. These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods. These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical modeling and inference. Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners.