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Mixture Modelling for Medical and Health Sciences

Author: Shu-Kay Ng
Publisher: Milton : CRC Press LLC, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Mixture Modelling for Medical and Health Sciences provides a direct connection between theoretical developments in mixture modelling and their applications in real world problems. The book describes the development of the most important concepts through comprehensive analyses of real and practical examples taken from real-life research problems in.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Ng, Shu-Kay.
Mixture Modelling for Medical and Health Sciences.
Milton : CRC Press LLC, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Shu-Kay Ng
ISBN: 9781482236774 148223677X
OCLC Number: 1101035853
Notes: 6.2 Fundamentals for GLMM Formulation and Derivation
Description: 1 online resource (315 pages)
Contents: Cover; Half Title; Series Page; Title Page; Copyright Page; Dedication; Contents; Preface; 1. Introduction; 1.1 Why Mixture Modelling is Needed; 1.1.1 Example 1.1: UCLA Example Data Set; 1.2 Fundamental Concepts of Finite Mixture Models; 1.2.1 Maximum Likelihood Estimation; 1.2.2 Spurious Clusters; 1.2.3 Determination of the Number of Components; 1.2.4 Identifiability of Mixture Distributions; 1.3 EM Algorithm; 1.3.1 Basic Principles of the EM Algorithm; 1.3.2 Formulation of Mixture Modelling as Incomplete-Data Problems; 1.3.3 Convergence and Initialization of the EM Algorithm 1.3.4 Provision of Standard Errors of Estimates1.4 Applications of Mixture Models in Medical and Health Sciences; 1.4.1 Overview of Book; 1.4.2 Sample Size Considerations for Mixture Models; 1.5 Computing Packages for Mixture Models; 1.5.1 R Programs; 1.5.2 Fortran Programs; 2. Mixture of Normal Distributions for Continuous Data; 2.1 Introduction; 2.2 E- and M-steps; 2.3 Diagnostic Procedures; 2.4 Example 2.1: Univariate Normal Mixtures; 2.5 Example 2.2: Multivariate Normal Mixtures; 2.6 Extensions of the Normal Mixture Model; 2.7 R Programs for Fitting Mixtures of Normal Distributions 3. Mixture of Gamma Distributions for Continuous Non-Normal Data3.1 Introduction; 3.2 E- and M-steps; 3.3 Diagnostic Procedures; 3.4 Example 3.1: Mixture of Gamma Regression Model; 3.5 Example 3.2: Mixture of Gamma Distributions for Clustering Cost Data; 3.6 Fortran Programs for Fitting Mixtures of Gamma Distributions; 4. Mixture of Generalized Linear Models for Count or Categorical Data; 4.1 Introduction; 4.2 Poisson Mixture Regression Model; 4.3 Zero-Inflated Poisson Regression Model; 4.4 Zero-Inflated Negative Binomial Regression Models 4.4.1 Example 4.1: Pancreas Disorder Length of Stay Data4.5 Score Tests for Zero-Inflation in Count Models; 4.5.1 Example 4.2: Revisit of the Pancreas Disorder LOS Data; 4.6 Mixture of Generalized Bernoulli Distributions; 4.6.1 E- and M-steps; 4.6.2 Cluster Analysis in Comorbidity Research; 4.6.3 Example 4.3: Australian National Health Survey Data; 4.7 Computing Programs for Fitting Mixture of Generalized Linear Models; 5. Mixture Models for Survival Data; 5.1 Introduction; 5.2 Application of Mixture Models in Survival Analysis; 5.3 Mixture Models of Parametric Survival Distributions 5.3.1 The EM Algorithm for Mixtures of Parametric Survival Models5.3.2 Example 5.1: Survival Mixture Modelling of Mortality Data; 5.4 Semi-Parametric Mixture Survival Models; 5.4.1 The ECM Algorithm; 5.4.2 Example 5.2: Survival Analysis of Competing-Risks Data; 5.5 Long-Term Survivor Mixture Models; 5.5.1 Example 5.3: Long-Term Survivors Mixture Model; 5.6 Diagnostic Procedures; 5.7 Fortran Programs for Fitting Mixtures of Survival Models; 6. Advanced Mixture Modelling with Random-Effects Components; 6.1 Why is Random Effects Modelling Needed?

Abstract:

Mixture Modelling for Medical and Health Sciences provides a direct connection between theoretical developments in mixture modelling and their applications in real world problems. The book describes the development of the most important concepts through comprehensive analyses of real and practical examples taken from real-life research problems in.

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