LEADER 05446nam 2200661Ia 450 001 9910841722703321 005 20170815115810.0 010 $a1-282-30763-0 010 $a9786612307638 010 $a0-470-31706-X 010 $a0-470-31790-6 035 $a(CKB)1000000000687574 035 $a(EBL)469964 035 $a(OCoLC)476298238 035 $a(SSID)ssj0000342906 035 $a(PQKBManifestationID)11304594 035 $a(PQKBTitleCode)TC0000342906 035 $a(PQKBWorkID)10287381 035 $a(PQKB)10197802 035 $a(MiAaPQ)EBC469964 035 $a(PPN)159314976 035 $a(EXLCZ)991000000000687574 100 $a19990507d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSimulation$b[electronic resource] $ea modeler's approach /$fJames R. Thompson 210 $aNew York $cWiley$d2000 215 $a1 online resource (332 p.) 225 1 $aWiley series in probability and statistics. Applied probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-25184-4 320 $aIncludes bibliographical references and index. 327 $aSimulation A Modeler's Approach; Contents; Preface; 1 The Generation of Random Numbers; 1.1. Introduction; 1.2. The Generation of Random Uniform Variates; 1.3. Latticing and Other Problems; Problems; References; 2 Random Quadrature; 2.1. Introduction; 2.2. Hit-or-h.iiss Monte Carlo; 2.3. Sample Mean Monte Carlo; 2.4. Control Variate Sampling; 2.5. Importance Sampling; 2.6. Stratification; 2.7. Antithetic Variates; 2.8, Least Squares Est.imators; 2.9. Evaluation of Multidimensional Integrals; 2.10. Stratification in Multidimensional Integration; 2.11. Wiener Measure and Brownian Motion 327 $aProblemsReferences; 3 Monte Carlo Solutions of Differential Equations; 3.1. Introduction; 3.2. Gambler's Ruin; 3.3. Solution of Simple Differential Equations; 3.4. Solution of the Fokker-Planck Equation; 3.5. The Dirichlet Problem; 3.6. Solution of General Elliptic Differential Equations; 3.7. Conclusions; Problems; References; 4 Markov Chains, Poisson Processes, and Linear Equations; 4.1. Discrete Markov hfodeling; 4.1.1. The Basic Model; 4.1.2. Saving the King; 4.1.3. Screening for Cancer; 4.2. Poisson Process Modeling; 4.3. Solving Systems of Linear Equations 327 $a4.3.1. An Inefficient Procedure4.3.2. An Algorithm Based on Jacobi Iteration; Problems; References; 5 SIMEST, SIMDAT, and Pseudoreality; 5.1. Computers Si, Models No; 5.2. The Bootstrap: A Dirac-Comb Density btimator; 5.3. SIh4DAT: 4 Smooth Resampling Algorithm; 5.3.1. The SIhIDAT Algorithm; 5.3.2. An Empirical Justification of SIMDAT; 5.4. SIMEST: .4n Oncological Example; 5.4.1. An Exploratory Prelude; 5.4.2. Model and Algorithms; Problems; References; 6 Models for Stocks and Derivatives; 6.1. Introduction; 6.2. Ito's Lemma; 6.3. A Geometric Brownian Model for Stocks; 6.4. Diversification 327 $a6.5. Negatively Correlated Portfotios6.6. Bear Jumps; 6.7. Options; 6.8. Getting Real: Simulation Analysis of Option Buying; 6.9. Conclusions; Problems; References; 7 Simulation Assessment of Multivariate and Robust Procedures in Statisticai Process Control; 7.1. Introduction; 7.2. A Cont.amination Model for SPC; 7.3. A Compound Test for Higher-Dimensional SPC Data; 7.4. Rank Testing with Higher-Dimensional SPC Data; 7.5. A Robust Estimation Procedure for Location in Higher Dimensions; Problems; Referencas; 8 Noise and Chaos; 8.1. Introduction; 8.2. The Discrete Logistic Model 327 $a8.3. A Chaotic Convection Model8.4. Conclusions; Problems; References; 9 Bayesian Approaches; 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 Resampling-Based Tests; 10.1. Introduction; 10.2. Fisher's Analysis of Darwin's Data; 10.3. A Bootstrap Approximation to Fisher's Nonparametric Test; 10.4. The Classical Randomization Test; 10.5. A Resampling-Based Sign Test; 10.6. A Resampling Approach to Confidence Intervds; 10.7. Resampling for Regression Model Selection 327 $a10.8. The Bootstrap as an All-Purpose Tool 330 $aA unique, integrated treatment of computer modeling and simulation ""The future of science belongs to those willing to make the shift to simulation-based modeling,"" predicts Rice Professor James Thompson, a leading modeler and computational statistician widely known for his original ideas and engaging style. He discusses methods, available to anyone with a fast desktop computer, for integrating simulation into the modeling process in order to create meaningful models of real phenomena. Drawing from a wealth of experience, he gives examples from trading markets, oncology, epidemiology, statist 410 0$aWiley series in probability and statistics.$pApplied probability and statistics. 606 $aExperimental design 606 $aMathematical models 606 $aMathematical statistics 615 0$aExperimental design. 615 0$aMathematical models. 615 0$aMathematical statistics. 676 $a003.3 700 $aThompson$b James R$g(James Robert),$f1938-$0141788 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910841722703321 996 $aSimulation$9509216 997 $aUNINA