Disease Evolution: Models, Concepts, and Data Analyses (Dimacs Series in Discrete Mathematics and Theoretical Computer Science)

Book Description

Infectious diseases are continuing to threaten humankind. While some diseases have been controlled, new diseases are constantly appearing. Others are now reappearing in forms that are resistant to drug treatments. A capacity for continual re-adaptation furnishes pathogens with the power to escape our control efforts through evolution. This makes it imperative to understand the complex selection pressures that are shaping and reshaping diseases. Modern models of evolutionary epidemiology provide powerful tools for creating, expressing, and testing such understanding.

Bringing together international leaders in the field, this volume offers a panoramic tour of topical developments in understanding the mechanisms of disease evolution. The volume's first part elucidates the general concepts underlying models of disease evolution. Methodological challenges addressed include those posed by spatial structure, stochastic dynamics, disease phases and classes, single- and multi-drug resistance, the heterogeneity of host populations and tissues, and the intricate coupling of disease evolution with between-host and within-host dynamics. The book's second part shows how these methods are utilized for investigating the dynamics and evolution of specific diseases, including HIV/AIDS, tuberculosis, SARS, malaria, and human rhinovirus infections.

This volume is particularly suited for introducing young scientists and established researchers with backgrounds in mathematics, computer science, or biology to the current techniques and challenges of mathematical evolutionary epidemiology.

Book Description

This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention.

The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis.

Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so that the reader can skip the software-oriented chapters and sections without breaking the logical flow.

From the reviews:

"Strengths of this book include its breadth of topics, excellent organization and clarity of writing...I highly recommend this book to my colleagues and students." - Justine Shults for the Journal of Biopharmaceutical Statistics, Issue 3, 2006

" Models for Discrete Longitudinal Data is an excellent choice for any statistician with an interest in analyzing discrete longitudinal data. It covers all of the theoretical and applied aspects in this area and is organized in such a way to serve as a handy reference guide for applied statisticians, especially those in biomedical fields. I learned a great deal from this book, and I recommend it highly to others." - John Williamson for the Journal of the American Statistical Association, September 2006

Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (Applied Optimization)

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Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (Applied Optimization)
Klaus Schittkowski
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 1402010796

Book Description

The main objective of the book is to give an overview of numerical methods to compute parameters of a dynamical model by a least squares fit of experimental data. The mathematical equations under consideration are explicit model functions or steady state systems in the simplest case, or responses of dynamical systems defined by ordinary differential equations, differential algebraic equations, partial differential equations, and partial differential algebraic equations (1D). Many different mathematical disciplines must be combined to find a solution, for example nonlinear programming, least squares optimization, systems of nonlinear equations, ordinary differential equations, discretization of partial differential equations, sensitivity analysis, automatic differentiation, and statistics.

Customer Reviews:

review.......2005-04-14

The book contains the numerical tools needed to fit the solution of a dynamical system to experimental data by varying some design parameters. These are optimization techniques, solvers for ordinary and partial differential equations, steady-state systems etc. A whole chapter is devoted to the question "what happens if somnething goes wrong" and gives recommendations how to avoid pitfalls. There is a collection of 12 realistic case studies with practical background and a collection of a large number of test problems coming with a CD and demonstration software.

As a practitioner, I enjoyed very much reading the book. I got an impression how the mathematical routines work and understood some of my own difficulties when fitting experimental data.

book review.......2003-12-04

This book has the very very brief introductions to various topics and it almost feels like reading a bibliography on the topic of numerical data fitting ... could not find anything worthwhile in it .... I found more useful and comprehensive information on this topic through google search on the internet ... I regret spending all that money on this book.

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A classic
A useful synthesis of standard advanced methods
a great book

Models for Discrete Data
Daniel Zelterman
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Hardcover

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ASIN: 0198524366

Book Description

Discrete or count data arise in experiments where the outcomes are countable and classified into unique, non-overlapping categories. This book describes the statistical models for evaluating such data. It provides an introduction for graduate students and a concise review for practitioners.
The book provides the first in-depth coverage of a number of topics, including the negative multinormal distribution, the many forms of hypergeometric distribution, and coordinate-free models. A detailed treatment of the issues of sample size and power are given in terms of exact inference and
asymptotic, non-central chi-squared approximations. Throughout the text, the author interweaves standard statistical software, particularly SAS, with practical examples and current theory.

Customer Reviews:

A classic.......2001-09-18

This book must be a part of your library. I've read it several times, and highly recommend it.

A useful synthesis of standard advanced methods.......2001-06-01

The only review for this book is apparently by the author himself who says it's a great book. As he might be slightly biased in favor of his own writings a dissonant view is called for. Actually, he's right: this is an excellent book that brings together -- and explains as clearly as can do -- how to analyze statistically situations with discrete outcomes, determined by a large number of factors and where the interaction between those factors is potentially non-linear. A must-read for people who know how to run these methods on data in SAS but have no clue about the rationale behind them.

a great book.......2001-03-08

This is a really good book. I have read it several times.

DZ

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A milestone in contingency table analysis

Discrete Multivariate Analysis
Yvonne M. Bishop , Stephen Fienberg , and Paul W. Holland
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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The Analysis of Cross-Classified Categorical Data

Generalized Linear Models, Second Edition (Monographs on Statistics and Applied Probability)

Accessories:

Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)

Bayesian Core: A Practical Approach to Computational Bayesian Statistics (Springer Texts in Statistics)

ASIN: 0387728058

Book Description

The scientist searching for structure in large systems of data finds inspiration in his own discipline, support from modern computing, and guidance from statistical models. Because large sets of data are likely to be complicated, and because so many approaches suggest themselves, a codification of techniques of analysis, regarded as attractive paths rather than as straitjackets, offers the scientist valuable directions to try. The literature on discrete multivariate analysis, although extensive, is widely scattered. This book brings that literature together in an organized way.

Customer Reviews:

A milestone in contingency table analysis.......2000-07-09

This book is the first well-organized treatise in contingency table analysis using log-linear models. Today it remains the best in the subject. It is extremely fun to read. Tons of examples clearly explained with the help of lots of graphs and tables. Mathematical notations and arguments are clean and elegant. The MIT Press also deserved high praise for making this book with top quality and affordable price to students. In a perfect world, all books in mathematics and statistics should be written and published this way. This book is a gem.

Foundations of Information and Knowledge Systems: 4th International Symposium, FoIKS 2006, Budapest, Hungary, February 14-17, 2006, Proceedings (Lecture Notes in Computer Science)

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Foundations of Information and Knowledge Systems: 4th International Symposium, FoIKS 2006, Budapest, Hungary, February 14-17, 2006, Proceedings (Lecture Notes in Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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ASIN: 3540317821

Book Description

This book constitutes the refereed proceedings of the 4th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2006 held in Budapest, Hungary in February 2006.

The 14 revised full papers presented together with 3 revised short papers and 1 invited paper were carefully reviewed and selected from 54 submissions. Among the topics covered are the theoretical foundations of information and knowledge systems, as well as mathematical fields such as discrete mathematics, combinatorics, logics and finite model theory, and applications thereof for research on database and knowledge base theory.

Mathematical Methods for Protein Structure Analysis and Design: Advanced Lectures (Lecture Notes in Computer Science)

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Mathematical Methods for Protein Structure Analysis and Design: Advanced Lectures (Lecture Notes in Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Biochemistry | Biological Sciences | Science | Subjects | Books

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ASIN: 3540401040

Book Description

The revised lectures presented in this volume are based on a course given by leading scholars at the C.I.M.E. Summer School held in Martina Franca, Italy, in July 2000.
The 11 revised chapters presented were carefully revised and cross-reviewed in order to provide competent coverage of mathematical methods for protein structure analysis and design. The topics addressed pinpoint major issues arising in the development and analysis of models, algorithms, and software tools devoted to the structure of proteins, all of which play crucial roles in structural genomics and proteomics.

Modeling discrete choice with uncertain data: an augmented MNL estimator.(multinomial logit model) : An article from: American Journal of Agricultural Economics

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Modeling discrete choice with uncertain data: an augmented MNL estimator.(multinomial logit model) : An article from: American Journal of Agricultural Economics
Daniel Hellerstein
Manufacturer: American Agricultural Economics Association
ProductGroup: Book
Binding: Digital
ASIN: B000ALNULU
Release Date: 2005-07-25

Book Description

This digital document is an article from American Journal of Agricultural Economics, published by American Agricultural Economics Association on February 1, 2005. The length of the article is 5169 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

From the author: Key words: multinomial logit, simulated estimator, UIV, UDV, uncertain choices, uncertain variables.

Citation Details
Title: Modeling discrete choice with uncertain data: an augmented MNL estimator.(multinomial logit model)
Author: Daniel Hellerstein
Publication: American Journal of Agricultural Economics (Refereed)
Date: February 1, 2005
Publisher: American Agricultural Economics Association
Volume: 87 Issue: 1 Page: 77(8)

Distributed by Thomson Gale

A note on the performance of Bover-Arellano discrete choice dynamic estimators [An article from: Economics Letters]

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A note on the performance of Bover-Arellano discrete choice dynamic estimators [An article from: Economics Letters]
Y. Ostrovsky
Manufacturer: Elsevier
ProductGroup: Book
Binding: Digital
ASIN: B000PBZWVQ

Book Description

This digital document is a journal article from Economics Letters, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This study employs a Monte Carlo method to highlight problems in the performance of Bover-Arellano discrete choice dynamic estimators which have been used in models with habit persistence.

An ordered generalised extreme value model with application to alcohol [An article from: Journal of Health Economics]

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An ordered generalised extreme value model with application to alcohol [An article from: Journal of Health Economics]
M.N. Harris , P. Ramful , and X. Zhao
Manufacturer: Elsevier
ProductGroup: Book
Binding: Digital
ASIN: B000PA9UVA

Book Description

This digital document is a journal article from Journal of Health Economics, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
An Ordered Generalised Extreme Value (OGEV) model by Small (1987) is proposed for application to ordered discrete choice data. Relative to conventional Ordered Probit/Logit (OP/OL) and Multinomial Logit (MNL) models, the OGEV model is flexible, is defined by random utility maximization, and allows for correlation across choices via unobservable individual characteristics according to locations of the choices in the ordering. The OGEV model is applied to unit record data from Australia to study the impacts of prices, income and demographic characteristics on levels of alcohol consumption. Model selection analysis suggests that OGEV is preferred to both OP and MNL for the application.

Book Description

This book provides a much-needed evaluation of the history of men's fashion advertising in the first half of the twentieth century. Undoubtedly one of the most fecund and complex periods in the history of menswear promotion, the period saw vast sums of money spent on advertising men's clothing. As Jobling expertly shows, the erotic charge in evidence in the representation of the buff bodies in Calvin Klein's 80's campaigns had much earlier antecedents. Looking well beyond issues of representation to broader socio-economic contexts, Jobling addresses an exciting range of discourses relating to professionalization, modernity, mass-communication and marketing, display and consumer psychology.

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