Stochastic Ordering and Dependence in Applied Probability

1. Front Matter

   Pages n1-viii

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2. Univariate Ordering

   • R. Szekli

   Pages 1-46

3. Multivariate Ordering

   • R. Szekli

   Pages 47-133

4. Dependence

   • R. Szekli

   Pages 135-172

5. Back Matter

   Pages 173-201

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This book is an introductionary course in stochastic ordering and dependence in the field of applied probability for readers with some background in mathematics. It is based on lectures and senlinars I have been giving for students at Mathematical Institute of Wroclaw University, and on a graduate course a.t Industrial Engineering Department of Texas A&M University, College Station, and addressed to a reader willing to use for example Lebesgue measure, conditional expectations with respect to sigma fields, martingales, or compensators as a common language in this field. In Chapter 1 a selection of one dimensional orderings is presented together with applications in the theory of queues, some parts of this selection are based on the recent literature (not older than five years). In Chapter 2 the material is centered around the strong stochastic ordering in many dimen­ sional spaces and functional spaces. Necessary facts about conditioning, Markov processes an"d point processes are introduced together with some classical results such as the product formula and Poissonian departure theorem for Jackson networks, or monotonicity results for some re­ newal processes, then results on stochastic ordering of networks, re~~ment policies and single server queues connected with Markov renewal processes are given. Chapter 3 is devoted to dependence and relations between dependence and ordering, exem­ plified by results on queueing networks and point processes among others.

• Jackson network
• Markov process
• Markov renewal process
• Martingal
• Martingale
• Random variable
• point process
• probability
• probability space

• Mathematical Institute, University of Wroclaw, Wroclaw, Poland

  R. Szekli

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