Articoli correlati a Stochastic Ordering and Dependence in Applied Probability...

Stochastic Ordering and Dependence in Applied Probability (Lecture Notes in Statistics): 97 - Brossura

Szekli, R.

 
9780387944500: Stochastic Ordering and Dependence in Applied Probability (Lecture Notes in Statistics): 97

Sinossi

This book presents an introductory course in stochastic orderings and dependence and their applications to queues and networks of queues. Readers are assumed to have a firm grounding in Lebesgue measure, conditional expectation, and martingales. Chapter 1 presents a collection of one-dimensional orderings with applications to the theory of queues. Chapter 2 extends these concepts to stochastic orderings in many dimensional spaces and functional spaces. Then results are given on stochastic ordering of networks, replacement policies, and single-server queues associated with Markov renewal processes. Finally, Chapter 3 is devoted to dependence and the relations between dependence and orderings, and it includes applications to queueing networks and point processes.

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Contenuti

1 Univariate Ordering.- 1.1 Construction of iid random variables.- 1.2 Strong ordering.- 1.3 Convex ordering.- 1.4 Conditional orderings.- 1.5 Relative inverse function orderings.- 1.6 Dispersive ordering.- 1.7 Compounding.- 1.8 Integral orderings for queues.- 1.9 Relative inverse orderings for queues.- 1.10 Loss systems.- 2 Multivariate Ordering.- 2.1 Strassen’s theorem.- 2.2 Coupling constructions.- 2.3 Conditioning.- 2.4 Markov processes.- 2.5 Point processes on R, martingales.- 2.6 Markovian queues and Jackson networks.- 2.7 Poissonian flows and product formula.- 2.8 Stochastic ordering of Markov processes.- 2.9 Stochastic ordering of point processes.- 2.10 Renewal processes.- 2.11 Comparison of replacement policies.- 2.12 Stochastically monotone networks.- 2.13 Queues with MR arrivals.- 3 Dependence.- 3.1 Association.- 3.2 MTP2.- 3.3 A general theory of positive dependence.- 3.4 Multivariate orderings and dependence.- 3.5 Negative association.- 3.6 Independence via uncorrelatedness.- 3.7 Association for Markov processes.- 3.8 Dependencies in Markovian networks.- 3.9 Dependencies in Markov renewal queues.- 3.10 Associated point processes.- A.- A.1 Probability spaces.- A.2 Distribution functions.- A.3 Examples of distribution functions.- A.4 Other characteristics of probability measures.- A.5 Random variables equal in distribution.- A.6 Bibliography.

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