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Dependence in Probability and Statistics

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Dependence in Probability and Statistics

Dependence in Probability and Statistics

- Collection Statistics for Industry and Technology

500 pages, parution le 31/07/2006

Résumé

This book gives an account of recent developments in the field of probability and statistics for dependent data. It covers a wide range of topics from Markov chain theory and weak dependence with an emphasis on some recent developments on dynamical systems, to strong dependence in times series and random fields. There is a section on statistical estimation problems and specific applications. The book is written as a succession of papers by field specialists, alternating general surveys, mostly at a level accessible to graduate students in probability and statistics, and more general research papers mainly suitable to researchers in the field.

Written for: Graduate students, researchers

L'auteur Patrice Bertail

Autres livres de Patrice Bertail

L'auteur Paul Doukhan

Paul Doukhan est professeur à l'université de Cergy-Pontoise.

Autres livres de Paul Doukhan

Sommaire

  • Weak dependence and related concepts
    • Regeneration-based statistics for Harris recurrent Markov chains (Patrice Bertail, Stéphan Clémençon)
    • Subgeometric ergodicity of Markov chains (Randal Douc, Eric Moulines, Philippe Soulier)
    • Limit theorems for dependent U-statistics (Herold Dehling)
    • Recent results on weak dependence for causal sequences. statistical applications to dynamic systems (Clémentine Prieur)
    • Parametrized Kantorovic-Rubintein theorem and application to the coupling of random variables (Jérôme Dedecker, Clémentine Prieur, Paul Raynaud De Fitte)
    • Exponential inequalities and estimation of conditional probabilities (V. Maume-Deschamps)
    • Martingale approximation of non adapted stochastic processes with nonlinear growth of variance (Dalibor Volný)
  • Strong dependence
    • Almost periodically correlated processes with long memory (Anne Philippe, Donatas Surgailis, Marie-Claude Viano)
    • Long memory random fields (Frédéric Lavancier)
    • Long memory in nonlinear processes (Rohit Deo, Mengchen Hsich, Clifford M. Hurvich, Philippe Soulier)
    • A LARCH (8) vector valued process (Paul Doukhan, Gilles Teyssière, Pablo Winant)
    • On a Szegö type limit theorem and the asymptotic theory of random sums, integrals and quadratic forms (Florin Avram, Murad S. Taqqu)
    • Aggregation of doubly stochastic interactive Gaussian processes and Toeplitz forms of U-statistics (Didier Dacunha-Castelle, Lisandro Fermín)
  • Statistical Estimation and Applications
    • On efficient inference in GARCH processes (Christian Francq, Jean-Michel Zakoïan)
    • Almost sure rate of convergence of maximum likelihood estimators for multidimensional diffusions (Dasha Loukianova, Oleg Loukianova)
    • Convergence rates for density estimators of weakly dependent time series (Nicolas Ragache, Olivier Wintenberger)
    • Variograms for spatial max-stable random fields (Dan Cooley, Philippe Naveau, Paul Poncet)
    • A non-stationary paradigm for the dynamics of multivariate financial returns (Stefano Herzel, Catalin Starica, Reha Tütüncü)
    • Multivariate non-linear regression with applications (Tata Subba Rao, Gyorgy Terdik)
    • Nonparametric estimator of a quantile function for the probability of event with repeated data (Claire Pinçon, Odile Pons)
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Caractéristiques techniques du livre "Dependence in Probability and Statistics"

  PAPIER
Éditeur(s) Springer
Auteur(s) Patrice Bertail, Paul Doukhan, Philippe Soulier
Collection Statistics for Industry and Technology
Parution 31/07/2006
Nb. de pages 500
Format 15,5 x 23,5
Couverture Broché
Poids 715g
Intérieur Noir et Blanc
EAN13 9780387317410
ISBN13 978-0-387-31741-0

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