经管之家App
让优质教育人人可得
立即打开
Statistics for Research【3rd.Edition】-2004(研究用统计学 第3版)
| 【作 者】 Shirley dowdy |
| 【出 版 社】 John Wiley & Sons 【书 号】 047126735X |
| 【出版日期】 2004 年1月 |
WILEY SERIES IN PROBABILITY AND STATISTICS
Established by WALTER A. SHEWHART and SAMUEL S. WILKS
Editors: David J. Balding, Noel A. C. Cressie, Nicholas I. Fisher,
Iain M. Johnstone, J. B. Kadane, Louise M. Ryan, David W. Scott,
Adrian F. M. Smith, Jozef L. Teugels
Editors Emeriti: Vic Barnett, J. Stuart Hunter, David G. Kendall
A complete list of the titles in this series appears at the end of this volume
CONTENTS
Preface to the Third Edition ix
Preface to the Second Edition xiii
Preface to the First Edition xv
1 The Role of Statistics 1
1.1 The Basic Statistical Procedure 1
1.2 The Scientific Method 11
1.3 Experimental Data and Survey Data 19
1.4 Computer Usage 20
Review Exercises 21
Selected Readings 22
2 Populations, Samples, and Probability Distributions 25
2.1 Populations and Samples 25
2.2 Random Sampling 27
2.3 Levels of Measurement 30
2.4 Random Variables and Probability Distributions 33
2.5 Expected Value and Variance of a Probability Distribution 39
Review Exercises 47
Selected Readings 47
3 Binomial Distributions 49
3.1 The Nature of Binomial Distributions 49
3.2 Testing Hypotheses 59
3.3 Estimation 70
3.4 Nonparametric Statistics: Median Test 77
Review Exercises 78
Selected Readings 80
4 Poisson Distributions 81
4.1 The Nature of Poisson Distributions 81
4.2 Testing Hypotheses 84
4.3 Estimation 87
4.4 Poisson Distributions and Binomial Distributions 90
Review Exercises 93
Selected Readings 94
v5 Chi-Square Distributions 95
5.1 The Nature of Chi-Square Distributions 95
5.2 Goodness-of-Fit Tests 104
5.3 Contingency Table Analysis 108
5.4 Relative Risks and Odds Ratios 117
5.5 Nonparametric Statistics: Median Test for Several Samples 121
Review Exercises 124
Selected Readings 125
6 Sampling Distribution of Averages 127
6.1 Population Mean and Sample Average 127
6.2 Population Variance and Sample Variance 132
6.3 The Mean and Variance of the Sampling Distribution of Averages 138
6.4 Sampling Without Replacement 143
Review Exercises 144
7 Normal Distributions 147
7.1 The Standard Normal Distribution 147
7.2 Inference From a Single Observation 152
7.3 The Central Limit Theorem 155
7.4 Inferences About a Population Mean and Variance 157
7.5 Using a Normal Distribution to Approximate Other Distributions 164
7.6 Nonparametric Statistics: A Test Based on Ranks 173
Review Exercises 176
Selected Readings 177
8 Student’s t Distribution 179
8.1 The Nature of t Distributions 179
8.2 Inference About a Single Mean 182
8.3 Inference About Two Means 190
8.4 Inference About Two Variances 197
8.5 Nonparametric Statistics: Matched-Pair and Two-Sample Rank Tests 204
Review Exercises 209
Selected Readings 210
9 Distributions of Two Variables 211
9.1 Simple Linear Regression 211
9.2 Model Testing 223
9.3 Inferences Related to Regression 233
9.4 Correlation 238
9.5 Nonparametric Statistics: Rank Correlation 250
9.6 Computer Usage 253
9.7 Estimating Only One Linear Trend Parameter 256
Review Exercises 262
Selected Readings 263
vi CONTENTS10 Techniques for One-way Analysis of Variance 265
10.1 The Additive Model 265
10.2 One-Way Analysis-of-Variance Procedure 272
10.3 Multiple-Comparison Procedures 283
10.4 One-Degree-of-Freedom Comparisons 294
10.5 Estimation 300
10.6 Bonferroni Procedures 303
10.7 Nonparametric Statistics: Kruskal–Wallis ANOVA for Ranks 309
Review Exercises 313
Selected Readings 314
11 The Analysis-of-Variance Model 317
11.1 Random Effects and Fixed Effects 317
11.2 Testing the Assumptions for ANOVA 324
11.3 Transformations 329
Review Exercises 337
Selected Readings 338
12 Other Analysis-of-Variance Designs 341
12.1 Nested Design 341
12.2 Randomized Complete Block Design 350
12.3 Latin Square Design 360
12.4 a b Factorial Design 368
12.5 a b c Factorial Design 376
12.6 Split-Plot Design 387
12.7 Split Plot with Repeated Measures 398
Review Exercises 407
Selected Readings 408
13 Analysis of Covariance 409
13.1 Combining Regression with ANOVA 409
13.2 One-Way Analysis of Covariance 413
13.3 Testing the Assumptions for Analysis of Covariance 418
13.4 Multiple-Comparison Procedures 423
Review Exercises 428
Selected Readings 429
14 Multiple Regression and Correlation 431
14.1 Matrix Procedures 431
14.2 ANOVA Procedures for Multiple Regression and Correlation 439
14.3 Inferences About Effects of Independent Variables 444
14.4 Computer Usage 451
14.5 Model Fitting 458
14.6 Logarithmic Transformations 475
14.7 Polynomial Regression 484
CONTENTS vii14.8 Logistic Regression 495
Review Exercises 507
Selected Readings 508
Appendix of Useful Tables 511
Answers to Most Odd-Numbered Exercises and All Review Exercises 603
Index
金融中的蒙特卡罗方法Monte Carlo Methods In Finance【DJVU格式】
作者: Peter Jaeckel
isbn: 047149741X
书名: Monte Carlo Methods in Finance
出版社: John Wiley & Sons
装帧: Hardcover
出版年: 2002-04-11
简介 · · · · · · An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available.
The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.
目录
Preface
Acknowledgements
Mathematical Notation
1 Introduction
2 The Mathematics Behind Monte Carlo Methods
2.1 A Few Basic Terms in Probability and Statistics
2.2 Monte Carlo Simulations
2.2.1 Monte Carlo Supremacy
2.2.2 Multi-dimensional Integration
2.3 Some Common Distributions
2.4 Kolmogorov's Strong Law
2.5 The Central Limit Theorem
2.6 The Continuous Mapping Theorem
2.7 Error Estimation for Monte Cm'1o Methods
2.8 The Feynman-Kac Theorem
2.9 The Moore-Penrose Pseudo-inverse
3 Stochastic Dynamics
3.1 Brownian Motion
3.2 It6's Lemma
3.3 Normal Processes
3.4 Lognormal Processes
3.5 The Markovian Wiener Process Embedding Dimension
3.6 Bessel Processes
3.7 Constant Elasticity Of Variance Processes
3.8 Displaced Diffusion
4 Process-driven Sampling
4.1 Strong versus Weak Convergence
4.2 Numerical Solutions
4.2.1 The Euler Scheme
4.2.2 The Milsrein Scheme
4.2.3 Transformations
4.2.4 Predictor-Corrector
4.3 Spurious Paths
4.4 Sta'ong Convergence for Euler and Milsrein
5 Correlation tnd Co-movement
5.1 Measures for Co-dependence
5.2 Copulte
5.2.1 The Gaussian Copula
5.2.2 The t-Copu!a
5.2.3 Archimedean Copulae
6 Salvaging a Linear Correlation Matrix
6. I Hypersphere Decomposition
6.2 Spectral Decomposition
6.3 Angular Decomposition of Lower Triangular Fova
6.4 Examples
6.5 Angular Coordinates on a Hypersphere of Unit Radius
7 Pseudo-random Numbers
7.1 Chaos
7.2 The Mid-square Method
7.3 Congruential Generation
7.4 Ran0 To Ran3
7.5 The Mersenne Twister
7.6 Which One to Use?
8 Low-discrepancy Numbers
8.1 Discrepancy
8.2 Halton Numbers
8.3 Sobol' Numbers
8.3.1 Primitive Polynomials Modulo Two
8.3.2 The Construction of Sobol' Numbers
8.3.3 The Gray Code
8.3.4 The Initialisation of Sobol' Numbers
8.4 Niederreiter (1988) Numbers
8.5 Pairwise Projections
8.6 Empirical Discrepancies
8.7 The Number of Iterations
8.8 Appendix
8.8.1 Explicit Formula for the L2-norm Discrepancy on the
Unit Hypercube
8.8.2 Expected L2-norm Discrepancy of Truly Random Numbers
9 Non-uniform Variates
9. I Inversion of the Cumulative Probability Function
9.2 Using a Sampler Density
9.2.1 Importance Sampling
9.2.2 Rejection Sampling
9.3 Normal Variates
9.3.1 The Box-Muller Method
9.3.2 The Neave Effect
9.4 Simulating Multivariate Copula Draws
10 Variance Reduction Techniques
10.1 Antithetic Sampling
10.2 Vail.ate Recycling
10.3 Control Variates
10.4 Stratified Sampling
10.5 Importance Sampling
10.6 Moment Matching
10.7 Latin Hypercube Sampling
10.8 Path Construction
10.8.1 Incremental
10.8.2 Spectral
10.8.3 The Brownian Bridge
10.8.4 A Comparison of Path Construction Methods
10.8.5 Multivariate Path Construction
10.9 Appendix
10.9.1 Eigenvalues and Eigenvectors
of a Discrete-time Covariance Matrix
10.9.2 The Conditional Distribution of the Brownian Bridge
11 Greeks
11.1 Importance Of Greeks
11.2 An Up-Out-Call Option
11.3 Finite Differencing with Path Recycling
11.4 Finite Differencing with Importance Sampling
11.5 Pathwise Differentiation
11.6 The Likelihood Ratio Method
11.7 Comparative Figures
11.8 Summary
11.9 Appendix
11.9.1 The Likelihood Ratio Formula for Vega
11.9.2 The Likelihood Ratio Formula for Rho
12 Monte Carlo in the BGM/J Framework
12.1 The Brace-Gatarek-Musiela/Jamshidian Market Model
12.2 Factorisation
12.3 Bermudan Swaptions
12.4 Calibration to European Swaptions
12.5 The Predictor-Corrector Scheme
12.6 Heuristics of the Exercise Boundary
12.7 Exercise Boundary Parametrisation
12.8 The Algorithm
12.9 Numerical Results
12.I0 Summary
13 Non-recombining Trees
13.1 Introduction
13.2 Evolving the Forward Rates
13.3 Optimal Simplex Alignment
13.4 Implementation
13.5 Convergence Perfmance
13.6 Variance Matching
13.7 Exact Martingale Conditioning
13.8 Clustering
I3.9 A Simple Example
13.10 Summary
14 Miscellanea
14.1 Interpolation of the Term Structure of Implied Volatility
14.2 Watch Your CPU Usage
14.3 Numerical Overflow and Underflow
14.4 A Single Number or a Convergence Diagram?
14.5 Embedded Path Creation
14.6 How Slow is v_.xp ( ) ?
14.7 Parallel Computing And Multi-threading
Bibliography
Index
[此贴子已经被作者于2008-12-28 16:55:08编辑过]
[分享]Statistics for Research【3rd.Edition】-2004(研究用统计学 第3版)
本附件包括：
扫码加好友，拉您进群
全部版块
我的主页
收藏
