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Decoupling From Dependence to Independence [Paperback]

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  • Category: Books (Mathematics)
  • Author:  Pe?a, Victor de la, Gin?, Evarist
  • Author:  Pe?a, Victor de la, Gin?, Evarist
  • ISBN-10:  1461268087
  • ISBN-10:  1461268087
  • ISBN-13:  9781461268086
  • ISBN-13:  9781461268086
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Feb-2012
  • Pub Date:  01-Feb-2012
  • SKU:  1461268087-11-SPRI
  • SKU:  1461268087-11-SPRI
  • Item ID: 100753561
  • List Price: $169.99
  • Seller: ShopSpell
  • Ships in: 5 business days
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  • Delivery by: Jul 04 to Jul 06
  • Notes: Brand New Book. Order Now.
A friendly and systematic introduction to the theory and applications. The book begins with the sums of independent random variables and vectors, with maximal inequalities and sharp estimates on moments, which are later used to develop and interpret decoupling inequalities. Decoupling is first introduced as it applies to randomly stopped processes and unbiased estimation. The authors then proceed with the theory of decoupling in full generality, paying special attention to comparison and interplay between martingale and decoupling theory, and to applications. These include limit theorems, moment and exponential inequalities for martingales and more general dependence structures, biostatistical implications, and moment convergence in Anscombe's theorem and Wald's equation for U--statistics. Addressed to researchers in probability and statistics and to graduates, the expositon is at the level of a second graduate probability course, with a good portion of the material fit for use in a first year course.Randomly Stopped Processes U-Statistics and Processes Martingales and BeyondDecoupling theory provides a general framework for analyzing problems involving dependent random variables as if they were independent. It was born in the early eighties as a natural continuation of martingale theory and has acquired a life of its own due to vigorous development and wide applicability. The authors provide a friendly and systematic introduction to the theory and applications of decoupling. The book begins with a chapter on sums of independent random variables and vectors, with maximal inequalities and sharp estimates on moments which are later used to develop and interpret decoupling inequalities. Decoupling is first introduced as it applies in two specific areas, randomly stopped processes (boundary crossing problems) and unbiased estimation (U-- statistics and U--processes), where it has become a basic tool in obtaining several definitive results. In particular, decoupling is alı
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