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Statistical Distributions 4ed_Catherine Forbes
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Contents Preface xvii 1. Introduction 1 2. Terms and Symbols 3 2.1 Probability, Random Variable, Variate, and Number 3 Probabilistic Experiment 3 Sample Space 3 Random Variable 3 Variate 3 Random Number 4 2.2 Range, Quantile, Probability Statement, and Domain 4 Range 4 Quantile 5 Probability Statement 5 Probability Domain 5 2.3 Distribution Function and Survival Function 5 Distribution Function 5 Survival Function 6 2.4 Inverse Distribution Function and Inverse Survival Function 7 Inverse Survival Function 8 2.5 Probability Density Function and Probability Function 8 2.6 Other Associated Functions and Quantities 9 3. General Variate Relationships 15 3.1 Introduction 15 3.2 Function of a Variate 15 3.3 One-to-One Transformations and Inverses 16 Inverse of a One-to-One Function 17 3.4 Variate Relationships Under One-to-One Transformation 17 Probability Statements 17 Distribution Function 17 Inverse Distribution Function 18 Equivalence of Variates 18 Inverse Function of a Variate 18 vii viii Contents 3.5 Parameters, Variate, and Function Notation 19 Variate and Function Notation 19 3.6 Transformation of Location and Scale 20 3.7 Transformation from the Rectangular Variate 20 3.8 Many-to-One Transformations 22 Symmetrical Distributions 22 4. Multivariate Distributions 24 4.1 Joint Distributions 24 Joint Range 24 Bivariate Quantile 24 Joint Probability Statement 24 Joint Probability Domain 25 Joint Distribution Function 25 Joint Probability Density Function 25 Joint Probability Function 25 4.2 Marginal Distributions 26 Marginal Probability Density Function and Marginal Probability Function 26 4.3 Independence 27 4.4 Conditional Distributions 28 Conditional Probability Function and Conditional Probability Density Function 28 Composition 29 4.5 Bayes’ Theorem 30 4.6 Functions of a Multivariate 30 5. Stochastic Modeling 32 5.1 Introduction 32 5.2 Independent Variates 32 5.3 Mixture Distributions 33 Finite Mixture 33 Infinite Mixture of Distributions 35 5.4 Skew-Symmetric Distributions 38 5.5 Distributions Characterized by Conditional Skewness 39 5.6 Dependent Variates 42 6. Parameter Inference 44 6.1 Introduction 44 6.2 Method of Percentiles Estimation 44 6.3 Method of Moments Estimation 45 6.4 Maximum Likelihood Inference 47 Properties of MLEs 47 Approximate Sampling Distribution for Fixed n 48 Contents ix 6.5 Bayesian Inference 50 Marginal Posteriors 51 7. Bernoulli Distribution 53 7.1 Random Number Generation 53 7.2 Curtailed Bernoulli Trial Sequences 53 7.3 Urn Sampling Scheme 54 7.4 Note 54 8. Beta Distribution 55 8.1 Notes on Beta and Gamma Functions 56 Definitions 56 Interrelationships 56 Special Values 57 Alternative Expressions 57 8.2 Variate Relationships 57 8.3 Parameter Estimation 59 8.4 Random Number Generation 60 8.5 Inverted Beta Distribution 60 8.6 Noncentral Beta Distribution 61 8.7 Beta Binomial Distribution 61 9. Binomial Distribution 62 9.1 Variate Relationships 64 9.2 Parameter Estimation 65 9.3 Random Number Generation 65 10. Cauchy Distribution 66 10.1 Note 66 10.2 Variate Relationships 67 10.3 Random Number Generation 68 10.4 Generalized Form 68 11. Chi-Squared Distribution 69 11.1 Variate Relationships 71 11.2 Random Number Generation 72 11.3 Chi Distribution 73 12. Chi-Squared (Noncentral) Distribution 74 12.1 Variate Relationships 75 x Contents 13. Dirichlet Distribution 77 13.1 Variate Relationships 77 13.2 Dirichlet Multinomial Distribution 78 14. Empirical Distribution Function 79 14.1 Estimation from Uncensored Data 79 14.2 Estimation from Censored Data 79 14.3 Parameter Estimation 81 14.4 Example 81 14.5 Graphical Method for the Modified Order-Numbers 81 14.6 Model Accuracy 83 15. Erlang Distribution 84 15.1 Variate Relationships 85 15.2 Parameter Estimation 85 15.3 Random Number Generation 85 16. Error Distribution 86 16.1 Note 87 16.2 Variate Relationships 87 17. Exponential Distribution 88 17.1 Note 89 17.2 Variate Relationships 91 17.3 Parameter Estimation 92 17.4 Random Number Generation 92 18. Exponential Family 93 18.1 Members of the Exponential Family 93 18.2 Univariate One-Parameter Exponential Family 93 18.3 Parameter Estimation 95 18.4 Generalized Exponential Distributions 95 Generalized Student’s t Distribution 95 Variate Relationships 96 Generalized Exponential Normal Distribution 96 Generalized Lognormal Distribution 96 Variate Relationships 97 Contents xi 19. Extreme Value (Gumbel) Distribution 98 19.1 Note 99 19.2 Variate Relationships 100 19.3 Parameter Estimation 101 19.4 Random Number Generation 101 20. F (Variance Ratio) or Fisher–Snedecor Distribution 102 20.1 Variate Relationships 103 21. F (Noncentral) Distribution 107 21.1 Variate Relationships 108 22. Gamma Distribution 109 22.1 Variate Relationships 110 22.2 Parameter Estimation 111 22.3 Random Number Generation 112 22.4 Inverted Gamma Distribution 112 22.5 Normal Gamma Distribution 112 22.6 Generalized Gamma Distribution 113 Variate Relationships 113 23. Geometric Distribution 114 23.1 Notes 115 23.2 Variate Relationships 115 23.3 Random Number Generation 116 24. Hypergeometric Distribution 117 24.1 Note 118 24.2 Variate Relationships 118 24.3 Parameter Estimation 118 24.4 Random Number Generation 119 24.5 Negative Hypergeometric Distribution 119 24.6 Generalized Hypergeometric Distribution 119 25. Inverse Gaussian (Wald) Distribution 120 25.1 Variate Relationships 121 25.2 Parameter Estimation 121 xii Contents 26. Laplace Distribution 122 26.1 Variate Relationships 124 26.2 Parameter Estimation 124 26.3 Random Number Generation 124 27. Logarithmic Series Distribution 125 27.1 Variate Relationships 126 27.2 Parameter Estimation 126 28. Logistic Distribution 127 28.1 Notes 128 28.2 Variate Relationships 128 28.3 Parameter Estimation 130 28.4 Random Number Generation 130 29. Lognormal Distribution 131 29.1 Variate Relationships 132 29.2 Parameter Estimation 134 29.3 Random Number Generation 134 30. Multinomial Distribution 135 30.1 Variate Relationships 136 30.2 Parameter Estimation 136 31. Multivariate Normal (Multinormal) Distribution 137 31.1 Variate Relationships 138 31.2 Parameter Estimation 138 32. Negative Binomial Distribution 139 32.1 Note 140 32.2 Variate Relationships 141 32.3 Parameter Estimation 142 32.4 Random Number Generation 142 33. Normal (Gaussian) Distribution 143 33.1 Variate Relationships 144 33.2 Parameter Estimation 147 Contents xiii 33.3 Random Number Generation 147 33.4 Truncated Normal Distribution 147 33.5 Variate Relationships 148 34. Pareto Distribution 149 34.1 Note 149 34.2 Variate Relationships 150 34.3 Parameter Estimation 151 34.4 Random Number Generation 151 35. Poisson Distribution 152 35.1 Note 153 35.2 Variate Relationships 153 35.3 Parameter Estimation 156 35.4 Random Number Generation 156 36. Power Function Distribution 157 36.1 Variate Relationships 157 36.2 Parameter Estimation 159 36.3 Random Number Generation 159 37. Power Series (Discrete) Distribution 160 37.1 Note 160 37.2 Variate Relationships 161 37.3 Parameter Estimation 161 38. Queuing Formulas 162 38.1 Characteristics of Queuing Systems and Kendall-Lee Notation 162 Characteristics of Queuing Systems 162 Kendall-Lee Notation 164 38.2 Definitions, Notation, and Terminology 164 Steady State 164 Traffic Intensity and Traffic Density 164 Notation and Terminology 164 38.3 General Formulas 166 38.4 Some Standard Queuing Systems 166 The M/M/1/G/∞/∞ System 166 The M/M/s/G/ ∞/∞ System 166 The M/G/1/G/∞/∞ System (Pollaczek-Khinchin) 168 The M/M/1/G/m/∞ System 168 The M/G/m/G/m/∞ System (Erlang) 169 xiv Contents The M/M/1/G/N/N System (One Server, Finite Population N) 169 The M/M/s/G/N/N System (s Servers, Finite Population N) 171 39. Rayleigh Distribution 173 39.1 Variate Relationships 173 39.2 Parameter Estimation 175 40. Rectangular (Uniform) Continuous Distribution 176 40.1 Variate Relationships 177 40.2 Parameter Estimation 179 40.3 Random Number Generation 179 41. Rectangular (Uniform) Discrete Distribution 180 41.1 General Form 181 41.2 Parameter Estimation 182 42. Student’s t Distribution 183 42.1 Variate Relationships 185 42.2 Random Number Generation 186 43. Student’s t (Noncentral) Distribution 187 43.1 Variate Relationships 188 44. Triangular Distribution 189 44.1 Variate Relationships 189 44.2 Random Number Generation 190 45. von Mises Distribution 191 45.1 Note 191 45.2 Variate Relationships 192 45.3 Parameter Estimation 192 46. Weibull Distribution 193 46.1 Note 195 46.2 Variate Relationships 196 46.3 Parameter Estimation 196 46.4 Random Number Generation 196 Contents xv 46.5 Three-Parameter Weibull Distribution 196 46.6 Three-Parameter Weibull Random Number Generation 198 46.7 Bi-Weibull Distribution 198 46.8 Five-Parameter Bi-Weibull Distribution 198 Bi-Weibull Random Number Generation 200 Bi-Weibull Graphs 200 46.9 Weibull Family 201 47. Wishart (Central) Distribution 202 47.1 Note 203 47.2 Variate Relationships 203 48. Statistical Tables 204 Table48.1: Normal Distribution Function −F N (x) 205 Table48.2: Percentiles of the Chi-Squared χ 2 : ν Distribution, G(1 − α) 206 Table48.3: Percentiles of the F : ν,ω Distribution 207 Table48.4: Percentiles of the Student’s t Distribution 209 Table48.5: Partial Expectations for the Standard Normal Distribution 210 Bibliography 211 |
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