Last edited by Vigar
Monday, July 27, 2020 | History

1 edition of Medical Applications of Finite Mixture Models found in the catalog.

Medical Applications of Finite Mixture Models

Peter Schlattmann

Medical Applications of Finite Mixture Models

by Peter Schlattmann

  • 275 Want to read
  • 28 Currently reading

Published by Springer-Verlag Berlin Heidelberg in Berlin, Heidelberg .
Written in English

    Subjects:
  • Epidemiology,
  • Mathematical statistics,
  • Statistics,
  • Public health

  • Edition Notes

    Statementby Peter Schlattmann
    SeriesStatistics for Biology and Health
    ContributionsSpringerLink (Online service)
    The Physical Object
    Format[electronic resource] /
    ID Numbers
    Open LibraryOL25561401M
    ISBN 109783540686507, 9783540686514

      This book aims to simulate some common medical problems using finite element advanced technologies, which establish a base for medical researchers to conduct further investigations. This book consists of four main parts: (1) bone, (2) soft tissues, (3) joints, and (4) implants. Finite mixture models using the fmm prefix: fmm: betareg: Finite mixtures of beta regression models: fmm: cloglog: Finite mixtures of complementary log-log regression models publishes books, manuals, and journals about Stata and general statistics topics for professional researchers of all disciplines. Contact us. Stata Press Lakeway.

    The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and. dynamic discrete choice models are estimated (e.g., Keane and Wolpin (), Cameron and Heckman ()). Heckman and Singer () used finite mix-tures to approximate more general mixture models in the context of duration models with unobserved heterogeneity. In most applications of finite mixture models, the components of the mixture.

      Finite mixture models, which are a type of latent variable model, express the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications, one assumes that the overall population heterogeneity with respect to a set of manifest variables results from the existence of two or more distinct homogeneous subgroups, or latent. The item Finite mixture models: review, applications, and computer-intensive methods, door José Manuel Gonçalves Dias represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Brigham Young University.


Share this book
You might also like
U.S. Department of Agriculture

U.S. Department of Agriculture

Text-typological discourse processing in the teaching of literature with special reference to English language teaching in Jordan.

Text-typological discourse processing in the teaching of literature with special reference to English language teaching in Jordan.

The anthropometry of the American Negro

The anthropometry of the American Negro

Forest Peak Research Natural Area.

Forest Peak Research Natural Area.

Century of inventions [microform].

Century of inventions [microform].

The DOL Womens Bureau Work and Family Clearinghouse

The DOL Womens Bureau Work and Family Clearinghouse

Blood lipids and lipoproteins

Blood lipids and lipoproteins

Miracle and machine

Miracle and machine

Cancer and its management

Cancer and its management

Medical Applications of Finite Mixture Models by Peter Schlattmann Download PDF EPUB FB2

The book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models. The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology. After an informal introduction the book provides and summarizes the mathematical background necessary Cited by: The book shows how Medical Applications of Finite Mixture Models book model heterogeneity in medical research with covariate adjusted finite mixture models.

The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology. After an informal introduction the book provides and summarizes the mathematical background necessary.

With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its.

A comprehensive treatment of medical applications of mixture models is provided in Schlattmann (). Recent reviews of the mixture model literature can. An up-to-date, comprehensive account of major issues in finite mixture modelingThis volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions.

With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book.

An up-to-date, comprehensive account of major issues in finite mixture modeling This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions.

With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book.

With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its.

Ruth King, Rachel McCrea, in Handbook of Statistics, Model M h(k). The model can be mathematically described as a finite mixture model on the individuals, where it is unknown which mixture, or subpopulation, each individual belongs to—such models were initially proposed by Pledger ().We assume that there are a total of k mixture components, such that an individual belongs to.

In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis.

The prominence of finite mixture modelling is greater than ever. Many important statistical topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity involve finite mixture models in some way or other.

The area of potential applications goes beyond simple data. An up-to-date, comprehensive account of major issues in finite mixture modeling This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book Reviews: 1.

The book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models. The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology.

With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its Cited by: This book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models.

The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its Reviews: 4.

Although this talk will focus on applications of –nite mixture models in health economics, such models appear in many literatures, often with di⁄erent names Finite mixture models have been used in studies of –nance marketing biology genetics astronomy arti–cial intelligence language processing philosophy Finite mixture models are also.

Structure General mixture model. A typical finite-dimensional mixture model is a hierarchical model consisting of the following components. N random variables that are observed, each distributed according to a mixture of K components, with the components belonging to the same parametric family of distributions (e.g., all normal, all Zipfian, etc.) but with different parameters.

The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature.

The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models.

Because of. Finite Mixture Models (, Statistical Methods in Medical Research) "The book by McLachlan and Peel is a masterly overview of the area, which manages the difficult task of integrating the technical and the practical almost seamlessly, so that the material included should appeal to a variety of statisticians ranging form those interested.

The –nite mixture model provides a natural representation of heterogeneity in a –nite number of latent classes It concerns modeling a statistical distribution by a mixture (or weighted sum) of other distributions Finite mixture models are also known as latent class models unsupervised learning models Finite mixture models are closely related to.

Testing for the Number of Components in Mixture Models 34 Brief History of Finite Mixture Models 35 Notation 37 2 ML Fitting of Mixture Models 40 Introduction 40 ML Estimation 40 Information Matrices 41 Asymptotic Covariance Matrix of MLE 42 Properties of MLEs for Mixture Models 42 Choice of Root 44 Gaussian mixture models are a probabilistic model for representing normally distributed subpopulations within an overall population.

Mixture models in general don't require knowing which subpopulation a data point belongs to, allowing the model to learn the subpopulations automatically. Since subpopulation assignment is not known, this constitutes a form of unsupervised learning.In this book, the authors give a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions.

This area of statistics is important to a range of disciplines, and it's methodology is attracting interest from researchers in .