Dependence in Probability and Statistics
Title:
Dependence in Probability and Statistics
Author:
Bertail, Patrice. editor.
ISBN:
9780387360621
Physical Description:
VIII, 490 p. 40 illus. online resource.
Series:
Lecture Notes in Statistics, 187
Contents:
Weak dependence and related concepts -- Regeneration-based statistics for Harris recurrent Markov chains -- Subgeometric ergodicity of Markov chains -- Limit Theorems for Dependent U-statistics -- Recent results on weak dependence for causal sequences. Statistical applications to dynamical systems. -- Parametrized Kantorovich-Rubinštein theorem and application to the coupling of random variables -- Exponential inequalities and estimation of conditional probabilities -- Martingale approximation of non adapted stochastic processes with nonlinear growth of variance -- Strong dependence -- Almost periodically correlated processes with long memory -- Long memory random fields -- Long Memory in Nonlinear Processes -- A LARCH(?) Vector Valued Process -- On a Szegö type limit theorem and the asymptotic theory of random sums, integrals and quadratic forms -- Aggregation of Doubly Stochastic Interactive Gaussian Processes and Toeplitz forms of U-Statistics -- Statistical Estimation and Applications -- On Efficient Inference in GARCH Processes -- Almost sure rate of convergence of maximum likelihood estimators for multidimensional diffusions -- Convergence rates for density estimators of weakly dependent time series -- Variograms for spatial max-stable random fields -- A non-stationary paradigm for the dynamics of multivariate financial returns -- Multivariate Non-Linear Regression with Applications -- Nonparametric estimator of a quantile function for the probability of event with repeated data.
Abstract:
This book gives a detailed account of some recent developments in the field of probability and statistics for dependent data. The book 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. A special section is devoted to statistical estimation problems and specific applications. The book is written as a succession of papers by some specialists of the field, 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. The first part of the book considers some recent developments on weak dependent time series, including some new results for Markov chains as well as some developments on new notions of weak dependence. This part also intends to fill a gap between the probability and statistical literature and the dynamical system literature. The second part presents some new results on strong dependence with a special emphasis on non-linear processes and random fields currently encountered in applications. Finally, in the last part, some general estimation problems are investigated, ranging from rate of convergence of maximum likelihood estimators to efficient estimation in parametric or non-parametric time series models, with an emphasis on applications with non-stationary data. Patrice Bertail is researcher in statistics at CREST-ENSAE, Malakoff and Professor of Statistics at the University-Paris X. Paul Doukhan is researcher in statistics at CREST-ENSAE, Malakoff and Professor of Statistics at the University of Cergy-Pontoise. Philippe Soulier is Professor of Statistics at the University-Paris X.
Added Corporate Author:
Electronic Access:
http://dx.doi.org/10.1007/0-387-36062-X