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El. knyga: Probability and Statistical Models: Foundations for Problems in Reliability and Financial Mathematics

  • Formatas: PDF+DRM
  • Išleidimo metai: 26-Aug-2010
  • Leidėjas: Birkhauser Boston Inc
  • Kalba: eng
  • ISBN-13: 9780817649876
  • Formatas: PDF+DRM
  • Išleidimo metai: 26-Aug-2010
  • Leidėjas: Birkhauser Boston Inc
  • Kalba: eng
  • ISBN-13: 9780817649876

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Probability models are now a vital componentof every scienti c investigation. This book is intended to introduce basic ideas in stochastic modeling, with emphasis on models and techniques. These models lead to well-known parametric lifetime distributions, such as exponential, Weibull, and gamma distributions, as well as the change-point and mixture models. They also motivate us to consider more general notions of nonparametric lifetime distribution classes. Particular attention has been paid to their applications in reliability, insurance mathematics, and economics. The following topics are the focus in this volume: 1. Exponential Distributions and the Poisson Process; 2. Parametric Lifetime Distributions; 3. Nonparametric Lifetime Distribution Classes; 4. Multivariate Exponential Extensions; 5. Association and Dependence; 6. Renewal Theory; 7. Applications to Reliability, Insurance, Finance, and Credit Risk. Chapter1providesnotationandbasicresultsinprobabilitytheorythatareneeded in the consequent chapters. Chapters 2 and 3 are devoted to models related to exponential distribution and Poisson processes. Particular attentions is paid to the characterizations of exponential distribution and the Poisson process. Two of the most important properties that characterize exponential distribution: the lack of memory property and constant failure rate are discussed in detail. Then the g- eralizations of exponential distribution are examined in three directions: through its parametric form that leads to parametric families of lifetime distributions; via notionsof aging(such as monotonefailure rate) that lead to a varietyof lifetime d- tribution classes; and through lifetime distributions of multiple component systems that lead to multivariate (mainly bivariate) exponential extension.

Recenzijos

From the reviews:

This is a nice introductory textbook on stochastic processes, basically devoted to the Poisson process and its variants. The basic results are well illustrated by many examples with many problems at the end of each chapter. The book is suitable for students that do not have an advanced training in the measure-theoretic aspects of probability or stochastic integration. (Henryk Gzyl, Zentralblatt MATH, Vol. 1215, 2011)

1 Preliminaries
1(22)
1.1 Introduction
1(1)
1.2 Notations
2(2)
1.3 Random Variable and Distribution Function
4(1)
1.4 Mean and Variance
5(3)
1.5 Joint and Conditional Distributions
8(5)
1.5.1 Joint Distribution
8(1)
1.5.2 Independent Sums and Laws
9(1)
1.5.3 Conditional Distribution and Mean
10(3)
1.6 Survival Function and Failure Rate
13(10)
1.6.1 Survival Function and Failure Rate
13(2)
1.6.2 Mean and Mean Residual Life
15(1)
1.6.3 Cauchy Functional Equation
16(1)
Problems
17(6)
2 Exponential Distribution
23(22)
2.1 Introduction
23(1)
2.2 Exponential Distribution
23(4)
2.3 Characterization of Exponential Distribution
27(5)
2.3.1 Memoryless Property
27(3)
2.3.2 Constant Failure Rate Function
30(1)
2.3.3 Extreme Value Distribution
30(2)
2.4 Order Statistics and Exponential Distribution
32(5)
2.4.1 Some Properties of Order Statistics
32(3)
2.4.2 Characterization Based on Order Statistics
35(2)
2.4.3 Record Values
37(1)
2.5 More Applications
37(8)
Problems
40(5)
3 Poisson Process
45(26)
3.1 Poisson Process as a Counting Process
45(2)
3.2 Characterization of Poisson Processes as Counting Processes
47(6)
3.3 Poisson Process as a Renewal Process
53(4)
3.4 Further Properties of Poisson Process
57(3)
3.4.1 Superposition Process
57(1)
3.4.2 Decomposition of Poisson Process
58(2)
3.5 Examples of Poisson Process
60(11)
Problems
67(4)
4 Parametric Families of Lifetime Distributions
71(16)
4.1 Weibull Distribution
71(3)
4.2 Gamma Distribution
74(4)
4.3 Change-Point Model
78(1)
4.4 Mixture Exponential Distribution
79(2)
4.5 IFR (DFR) and Mixture Erlang Distribution
81(6)
Problems
84(3)
5 Lifetime Distribution Classes
87(30)
5.1 IFR and DFR
87(5)
5.1.1 IFR and PF2
87(3)
5.1.2 Smoothness of IFR Distribution
90(1)
5.1.3 A Sufficient Condition
91(1)
5.2 IFRA and DFRA Classes
92(3)
5.3 Several Lifetime Distribution Classes
95(4)
5.4 Preservation of Lifetime Distributions Under Reliability Operations
99(5)
5.4.1 Independent Sums
99(2)
5.4.2 Mixture of Lifetime Distributions
101(3)
5.5 Shock Models and Lifetime Distribution Classes
104(13)
5.5.1 IFRA Property of Shock Model
104(3)
5.5.2 Extension of Cumulative Damage Model
107(1)
5.5.3 General Cumulative Damage Model
108(2)
5.5.4 Shock Models Leading to Other Lifetime Distributions
110(2)
Problems
112(5)
6 Multivariate Lifetime Distributions
117(24)
6.1 Basic Properties of Bivariate Distributions
117(3)
6.2 Bivariate Memoryless Property
120(5)
6.3 Properties of the BVE
125(8)
6.4 A Nonfatal Shock Model
133(2)
6.5 Absolutely Continuous Bivariate Exponential Extensions
135(6)
Problems
139(2)
7 Association and Dependence
141(18)
7.1 Several Concepts of Association
141(5)
7.2 MTP2 Distribution
146(3)
7.3 Multivariate Failure Rate and Distribution Class
149(2)
7.4 Negative Association
151(8)
Problems
156(3)
8 Renewal Theory
159(20)
8.1 Renewal Theorem
159(4)
8.2 High-Order Approximations and Bounds
163(3)
8.3 Delayed Renewal Process
166(3)
8.4 Defective Renewal Process
169(10)
Problems
175(4)
9 Risk Theory
179(20)
9.1 Classical Risk Model
179(2)
9.2 Approximation and Bounds for Ruin Probability
181(2)
9.3 Deficit at Ruin
183(2)
9.4 Large Claim Case
185(8)
9.4.1 Bounds in terms of NWU (NBU) Distribution Classes
186(4)
9.4.2 Subexponential Classes
190(3)
9.5 Risk Sharing and Stop-Loss Reinsurance
193(6)
Problems
196(3)
10 Asset Pricing Theory
199(22)
10.1 Utility, Risk, and Pricing Kernel
199(4)
10.1.1 Utility and Risk
199(1)
10.1.2 Asset Pricing Formula and Pricing Kernel
200(3)
10.2 Models for Returns
203(2)
10.2.1 β-Representation
203(1)
10.2.2 Frontier Expression
204(1)
10.2.3 Log-Normal Model
204(1)
10.3 Examples of Risk Assets
205(2)
10.4 Risk-Neutral Probabilities
207(1)
10.5 Option Pricing for Binomial Model
208(2)
10.5.1 Pricing Formula for Multiple Stages
208(1)
10.5.2 Binomial Model
208(2)
10.6 Portfolio Management
210(6)
10.6.1 Discrete Financial Market
210(1)
10.6.2 Risk Management
211(2)
10.6.3 Hedging Options
213(3)
10.7 Black-Scholes Formula
216(5)
Problems
218(3)
11 Credit Risk Modeling
221(16)
11.1 Two Models for Default Probability
221(4)
11.1.1 Basic Notation
221(1)
11.1.2 Reduced Form
222(2)
11.1.3 Structural Model
224(1)
11.2 Valuation of Default Risk
225(2)
11.2.1 No Recovery Zero-Coupon Defaultable Bond
226(1)
11.2.2 Non-Zero Recovery
226(1)
11.2.3 Actual and Risk Neutral Default Intensity
227(1)
11.3 Credit Rating: Default and Transition
227(3)
11.3.1 Credit Rating
227(2)
11.3.2 Rating Assignment
229(1)
11.3.3 Rating Transition
229(1)
11.4 Correlated Defaults
230(2)
11.4.1 Credit Metrics
230(1)
11.4.2 Correlated Default Intensities
231(1)
11.4.3 Copula-Based Correlation Modeling
231(1)
11.5 Credit Derivatives
232(5)
11.5.1 Credit Default Swaps
233(1)
11.5.2 Collateral Debt Obligations
234(1)
Problems
235(2)
Bibliographical Notes and Further Reading 237(4)
References 241(4)
Answers and Solutions to Selected Problems 245(20)
Index 265
Arjun K. Gupta is the author of a previous Birkhäuser book: Gupta/Chen, "Parametric Statistical Change Point Analysis," (978-0-8176-4169-6, 2000, 184 p.)