Empirical model building: data, models, and reality
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, N.J.
Wiley
2011
|
| Edition: | 2. ed. |
| Series: | Wiley series in probability and statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVII, 430 S. graph. Darst. |
| ISBN: | 9780470467039 |
Staff View
MARC
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| 245 | 1 | 0 | |a Empirical model building |b data, models, and reality |c James R. Thompson |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Hoboken, N.J. |b Wiley |c 2011 | |
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Record in the Search Index
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Contents
Preface
xiii
1. Models
of Growth and Decay
1
1.1.
A Simple Pension and Annuity Plan
1
1.2.
Income Tax Bracket Creep and the Quiet Revolution of
1980 8
1.3.
Retirement of a Mortgage
16
1.4.
Some Mathematical Descriptions of the Theory of
Malthus
18
1.5.
Metastasis and Resistance
29
Problems
37
References
38
2.
Models of Competition, Survival, and Combat
39
2.1.
An Analysis of the Demographics of Ancient Israel
39
2.2.
The Plague and John Graunt's Life Table
44
2.3.
Modular Data-Based Wargaming
48
2.3.1.
Herman
Kahn
and the Winning of the Cold War
64
2.4.
Prédation
and Immune Response Systems
65
2.5.
Pyramid Clubs for Fun and Profit
69
Problems
72
References
74
3.
Epidemics
75
3.1.
Introduction
75
3.2.
John Snow and the London Cholera Epidemic of
1854 77
3.3.
Prelude: The Postwar Polio Epidemic
79
3.4.
AIDS: A New Epidemic for America
80
3.5.
Why an AIDS Epidemic in America?
82
3.5.1.
Political Correctness Can Kill
84
3.6.
The Effect of the Gay Bathhouses
85
3.7.
A More Detailed Look at the Model
88
3.8.
Forays into the Public Policy Arena
91
3.9.
Modeling the Mature Epidemic
92
vii
Vlil
CONTENTS
3.10.
AIDS as a Facilitator of Other Epidemics
95
3.11.
Comparisons with First World Countries
95
3.12.
Conclusions: A Modeler's Portfolio
103
Problems
106
References
109
4.
Bootstrapping 111
4.1.
Introduction
111
4.2.
Bootstrapping Analysis of Darwin's Data
112
4.3.
A Bootstrapping Approximation to Fisher's Nonparametric Test
116
4.4.
A Resampling Bassed Sign Test
119
4.5.
A Bootstrapping Approach for Confidence Intervals
121
4.6.
Solving Ill-Structured Problems
122
Problems
128
References
130
5.
Monte Carlo Solutions of Differential Equations
131
5.1.
Introduction
131
5.2.
Gambler's Ruin
134
5.3.
Solution of Simple Differential Equations
137
5.4.
Solution oftheFokker-Planck Equation
138
5.5.
The Dirichlet Problem
139
5.6.
Solution of General Elliptic Differential Equations
142
5.7.
Conclusions
144
Problems
145
References
146
6.
SIMEST, SIMDAT, and Pseudoreality
147
6.1.
Introduction
147
6.2.
The Bootstrap: A Dirac-Comb Density Estimator
149
6.3.
SIMDAT: A Smooth Resampling Algorithm
152
6.3.1.
The SIMDAT Algorithm
153
6.3.2.
An Empirical Justification of SIMDAT
153
6.4.
SIMEST: An Oncological Example
156
6.4.1.
An Exploratory Prelude
158
6.4.2.
Models and Algorithms
158
Problems
170
References
172
7.
Exploratory Data Analysis
175
7.1.
Introduction
175
7.2.
Smoothing
176
7.3.
The Stem and Leaf Plot
187
7.4.
The Five Figure Summary
188
CONTENTS
IX
7.5.
Tukey's Box Plot
189
Problems
192
References
192
8.
Noise Killing Chaos
193
8.1.
Introduction
193
8.2.
The Discrete Logistic Model
196
8.3.
A Chaotic Convection Model
201
8.4.
Conclusions
204
Problems
208
References
208
9.
A Primer in Bayesian Data Analysis
209
9.1.
Introduction
209
9.2.
The EM Algorithm
210
9.3.
The Data Augmentation Algorithm
213
9.4.
The Gibbs Sampler
219
9.5.
Conclusions
224
Problems
225
References
228
10.
Multivariate and Robust Procedures in Statistical Process Control
229
10.1.
Introduction
229
10.2.
A Contamination Model for
SPC
231
10.3.
A Compound Test for Higher Dimensional
SPC Data
236
10.4.
Rank Testing with Higher Dimensional
SPC Data
239
10.5.
A Robust Estimation Procedure for Location in Higher Dimensions
243
Problems
246
References
248
11.
Considerations for Optimization and Estimation in the Real
249
(Noisy) World
11.1.
Introduction
249
11.2.
The Nelder-Mead Algorithm
253
11.3.
The Box-Hunter Algorithm
256
11.4.
Simulated Annealing
262
11.5.
Exploration and Estimation in High Dimensions
264
Problems
270
References
271
12.
Utility and Group Preference
273
12.1.
Introduction
273
12.2.
The St. Petersburg Paradox
274
CONTENTS
12.3. von Neumann-Morgenstern Utility
282
12.4.
Creating a St.
Petersburg Trust
287
12.5.
Some
Problems
with
Aggregate
Choice Behavior
290
12.6. Jeffersonian
Realities
293
12.7.
Conclusions
300
Problems
300
References
303
A Primer in Sampling
305
13.1.
Introduction
305
13.1.1.
Tracking Pol Is
308
13.2.
Stratification
309
13.2.1.
A Warehouse Inventory
311
13.3.
The Saga of Happy Valley
312
13.3.1.
The Problem with Standards
313
13.4.
To Annex or Not
316
13.4.1.
The Bootstrap: The World in a Drop of Water
316
13.4.2.
Resampling and Histograms
317
13.5.
Using Sampling to Estimate Total Population Size
321
13.5.1.
Capture Recapture
322
Problems
324
References
325
14.
Stock Market: Strategies Based on Data versus Strategies
Based on Ideology
327
14.1.
Introduction
327
14.2.
Markowitz's Efficient Frontier: Portfolio Design as Constrained
329
Optimization
14.3.
Sharpe's Super Efficient Frontier: The Capital Market Line
333
(CAPM)
14.4.
The Security Market Line
335
14.5.
The
Sharpe
Diagonal Model
337
14.6.
Portfolio Evaluation and the Capital Asset Pricing Model
339
14.7.
Views of Risk
342
14.7.1.
Diversification
344
14.8.
Stock Progression as Geometric Brownian Motion
344
14.8.1.
Ito's Lemma
344
14.8.2.
A Geometric Brownian Model for Stocks
346
14.9.
Estimating
μ
and
σ
346
14.10.
The Time Indexed Distribution of Portfolio Value
348
14.11.
Negatively Correlated Portfolios
351
14.12.
Bear Jumps
354
14.13.
"Everyman's" MaxMedian Rule for Portfolio Manangement
357
14.13.1.
Investing in a 401k
360
CONTENTS
ХІ
14.14. Derivatives 363
14.14.1. Black-Scholes
and the Search for Risk Neutrality
364
14.14.2.
A Game Independent of the Odds
3 65
14.14.3.
The Black-Scholes Hedge Using Binomial Trees
368
14.15.
The Black-Scholes Derivation Using Differential Equations
374
14.16.
Black-Scholes: Some Limiting Cases
376
14.17.
Conclusions
381
Problems
382
References
384
Appendix A. A Brief Introduction to Probability and Statistics
387
A.
1.
Craps: An Intuitive Introduction to Probability and Statistics
387
A.
1.1.
Random Variables, Their Means and Variances
387
A.2. Combinatorics Basics
393
A.3. Bayesian Statistics
394
A.3.1.
Bayes'
Theorem
394
A.3.2. A Diagnostic Example
396
A.4. The Binomial Distribution
397
A.5. The Uniform Distribution
399
A.6. Moment Generating Functions
401
A.7. The Normal (Gaussian) Distribution
402
A.8. The Central Limit Theorem
405
A.9. The Gamma Distribution
406
A.
10.
Conditional Density Functions
407
A.ll. The Weak Law of Large Numbers
409
A.
12.
The Multivariate Normal Distribution
411
A.
13.
The Wiener Process
411
A.
14.
The
Poisson
Process and the
Poisson
Distribution
412
A.14.1. Simulating Bear Jumps
414
A.15. Parametric Simulation
415
A.
15.1.
Simulating a Geometric Brownian Walk
416
A.
15.2.
The Multivariate Case
417
A.
16.
Resampling Simulation
420
A.
16.1.
The Multivariate Case
421
A.17. A Portfolio Case Study
422
Appendix B. Statistical Tables
425
Tables of the Normal Distribution
425
Tables of the Chi-Square Distribution
426
Index
427 |
| any_adam_object | 1 |
| author | Thompson, James R. 1938-2017 |
| author_GND | (DE-588)136262481 |
| author_facet | Thompson, James R. 1938-2017 |
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| ctrlnum | (OCoLC)796253272 (DE-599)BVBBV040193634 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Soziologie Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Thompson, James R. 1938-2017 Verfasser (DE-588)136262481 aut Empirical model building data, models, and reality James R. Thompson 2. ed. Hoboken, N.J. Wiley 2011 XVII, 430 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Modèle mathématique Modèles mathématiques Modélisation Plan d'expérience Statistique mathématique Mathematisches Modell Experimental design Mathematical models Mathematical statistics Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf Modellierung (DE-588)4170297-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf (DE-588)4522595-3 Fallstudiensammlung gnd-content Statistisches Modell (DE-588)4121722-6 s DE-604 Modellierung (DE-588)4170297-9 s Statistik (DE-588)4056995-0 s Sozialwissenschaften (DE-588)4055916-6 s Mathematisches Modell (DE-588)4114528-8 s Erscheint auch als Online-Ausgabe 978-1-118-10965-6 Erscheint auch als Online-Ausgabe, PDF 978-1-118-10962-5 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025050167&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Thompson, James R. 1938-2017 Empirical model building data, models, and reality Modèle mathématique Modèles mathématiques Modélisation Plan d'expérience Statistique mathématique Mathematisches Modell Experimental design Mathematical models Mathematical statistics Sozialwissenschaften (DE-588)4055916-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Statistisches Modell (DE-588)4121722-6 gnd Modellierung (DE-588)4170297-9 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4114528-8 (DE-588)4121722-6 (DE-588)4170297-9 (DE-588)4056995-0 (DE-588)4522595-3 |
| title | Empirical model building data, models, and reality |
| title_auth | Empirical model building data, models, and reality |
| title_exact_search | Empirical model building data, models, and reality |
| title_full | Empirical model building data, models, and reality James R. Thompson |
| title_fullStr | Empirical model building data, models, and reality James R. Thompson |
| title_full_unstemmed | Empirical model building data, models, and reality James R. Thompson |
| title_short | Empirical model building |
| title_sort | empirical model building data models and reality |
| title_sub | data, models, and reality |
| topic | Modèle mathématique Modèles mathématiques Modélisation Plan d'expérience Statistique mathématique Mathematisches Modell Experimental design Mathematical models Mathematical statistics Sozialwissenschaften (DE-588)4055916-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Statistisches Modell (DE-588)4121722-6 gnd Modellierung (DE-588)4170297-9 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Modèle mathématique Modèles mathématiques Modélisation Plan d'expérience Statistique mathématique Mathematisches Modell Experimental design Mathematical models Mathematical statistics Sozialwissenschaften Statistisches Modell Modellierung Statistik Fallstudiensammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025050167&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT thompsonjamesr empiricalmodelbuildingdatamodelsandreality |