Quantitative medical data analysis using mathematical tools and statistical techniques / editors, Don Hong, Yu Shyr.

Format:

Book

Contributor:

Hong, Don.

Shyr, Yu.

Language:

English

Subjects (All):

Medical informatics.

Medicine--Data processing.

Medicine.

Physical Description:

1 online resource (364 p.)

Place of Publication:

Hackensack, NJ : World Scientific, c2007.

Language Note:

English

Summary:

Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis.This book gives a

Contents:

Preface; CONTENTS; Unit I. Statistical Methodology and Stochastic Modeling; Chapter 1 An Overview on Variable Selection for Longitudinal Data John J. Dziak and Runze Li; 1. Introduction; 2. Variable Selection for Linear Mixed Effects Models; 2.1. Fixed effects variable selection; 2.2. Random effects covariate selection; 3. Covariance Selection; 4. Variable Selection for GEE Model Fitting; 4.1. Extensions of traditional variable selection procedures; 4.2. Penalized GEE; 5. Numerical Comparison; 6. Variable Selection for Other Models; Acknowledgements; References

Chapter 2 Some Recent Results in Model Selection Xiaoming Huo and Xuelei (Sherry) Ni1. Introduction; 2. Our Perspective; 3. Formulation and Related Literature; 3.1. Subset Selection Criteria and (P0); 3.2. Greedy Algorithms and (P1); 4. Case Study; 4.1. An Extreme Example for the Least Angle Regressions; 4.1.1. Standardized Covariates; 4.1.2. To Create a Dramatic Presentation; 4.2. Variable Selection with Orthogonal Model Matrix; 5. Other Topics; 5.1. Computing Versus Statistical Properties; 5.2. Other Works in Variable Selection; 5.3. Back Elimination

5.4. Other Greedy Algorithms and Absolutely Optimal Subset in Variable Selection5.5. Model Selection versus Variable Selection; 5.6. Beyond Model Selection; 5.7. Beyond Ordinary Linear Regression; 5.8. Bayesian Approach; 5.9. Other Related Topics; Acknowledgments; References; Chapter 3 Some State Space Models of AIDS Epidemiology in Homosexual Populations Wai Y. Tan; 1. Introduction; 2. The State Space Models and the Generalized Bayesian Approach; 2.1. Some Advantages of the State Space Models

2.2. A General Bayesian Procedure for Estimating Unknown Parameters and State Variables via State Space Models3. Stochastic Models of AIDS Epidemic in Homosexual and Bisexual Populations; 3.1. The Stochastic Difference Equations for the State Variables; 3.2. The Probability Distributions of X(t) = {S(t), I(r; t), r = 0, 1,...,t}; 4. A State Space Model of AIDS Epidemic in Homosexual Populations; 4.1. The Stochastic System Model, the Augmented State Variables and Probability Distributions; 4.2. The Observation Model

4.3. The Posterior Distribution of the Unknown Parameters and State Variables4.4. The Generalized Bayesian Method for Estimating Unknown Parameters and State Variables; 5. Some Illustrative Examples; 5.1. The Initial Size; 5.2. The Prior Distributions of pS(t) and (u); 6. Conclusions; References; Chapter 4 Stochastic and State Space Models of Carcinogenesis: Some New Modeling Approaches Wai Y. Tan and L. J. Zhang; 1. Introduction; 2. Some Recent Cancer Biology for Modeling Carcinogenesis; 2.1. The Multi-Staging and Sequential Nature of Carcinogenesis; 2.2. The Genetic Changes and Cancer Genes

2.3. Epigenetic and Cancer

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9786611911935

9781281911933

1281911933

9789812772121

981277212X