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Empirical model building : data, models, and reality /
"This book presents a hands-on approach to the basic principles of empirical model building through the shrewd mixture of differential equations, computer-intensive methods, and data in a single-volume. It includes a series of real-world statistical problems illustrating modeling skills and tec...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
John Wiley & Sons,
©2011.
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| Edición: | 2nd ed. |
| Colección: | Wiley series in probability and statistics ;
794. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Empirical Model Building: Data, Models, and Reality; Contents; Preface; 1. Models of Growth and Decay; 1.1. A Simple Pension and Annuity Plan; 1.2. Income Tax Bracket Creep and the Quiet Revolution of 1980; 1.3. Retirement of a Mortgage; 1.4. Some Mathematical Descriptions of the Theory of Malthus; 1.5. Metastasis and Resistance; Problems; References; 2. Models of Competition, Survival, and Combat; 2.1. An Analysis of the Demographics of Ancient Israel; 2.2. The Plague and John Graunt's Life Table; 2.3. Modular Data-Based Wargaming; 2.3.1. Herman Kahn and the Winning of the Cold War.
- 2.4. Predation and Immune Response Systems2.5. Pyramid Clubs for Fun and Profit; Problems; References; 3. Epidemics; 3.1. Introduction; 3.2. John Snow and the London Cholera Epidemic of 1854; 3.3. Prelude: The Postwar Polio Epidemic; 3.4. AIDS: A New Epidemic for America; 3.5. Why an AIDS Epidemic in America?; 3.5.1. Political Correctness Can Kill; 3.6. The Effect of the Gay Bathhouses; 3.7. A More Detailed Look at the Model; 3.8. Forays into the Public Policy Arena; 3.9. Modeling the Mature Epidemic; 3.10. AIDS as a Facilitator of Other Epidemics; 3.11. Comparisons with First World Countries.
- 3.12. Conclusions: A Modeler's PortfolioProblems; References; 4. Bootstrapping; 4.1. Introduction; 4.2. Bootstrapping Analysis of Darwin's Data; 4.3. A Bootstrapping Approximation to Fisher's Nonparametric Test; 4.4. A Resampling Bassed Sign Test; 4.5. A Bootstrapping Approach for Confidence Intervals; 4.6. Solving Ill-Structured Problems; Problems; References; 5. Monte Carlo Solutions of Differential Equations; 5.1. Introduction; 5.2. Gambler's Ruin; 5.3. Solution of Simple Differential Equations; 5.4. Solution of the Fokker-Planck Equation; 5.5. The Dirichlet Problem.
- 5.6. Solution of General Elliptic Differential Equations5.7. Conclusions; Problems; References; 6. SIMEST, SIMDAT, and Pseudoreality; 6.1. Introduction; 6.2. The Bootstrap: A Dirac-Comb Density Estimator; 6.3. SIMDAT: A Smooth Resampling Algorithm; 6.3.1. The SIMDAT Algorithm; 6.3.2. An Empirical Justification of SIMDAT; 6.4. SIMEST: An Oncological Example; 6.4.1. An Exploratory Prelude; 6.4.2. Models and Algorithms; Problems; References; 7. Exploratory Data Analysis; 7.1. Introduction; 7.2. Smoothing; 7.3. The Stem and Leaf Plot; 7.4. The Five Figure Summary; 7.5. Tukey's Box Plot; Problems.
- References8. Noise Killing Chaos; 8.1. Introduction; 8.2. The Discrete Logistic Model; 8.3. A Chaotic Convection Model; 8.4. Conclusions; Problems; References; 9. A Primer in Bayesian Data Analysis; 9.1. Introduction; 9.2. The EM Algorithm; 9.3. The Data Augmentation Algorithm; 9.4. The Gibbs Sampler; 9.5. Conclusions; Problems; References; 10. Multivariate and Robust Procedures in Statistical Process Control; 10.1. Introduction; 10.2. A Contamination Model for SPC; 10.3. A Compound Test for Higher Dimensional SPC Data; 10.4. Rank Testing with Higher Dimensional SPC Data.
- 10.5. A Robust Estimation Procedure for Location in Higher Dimensions.