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Dedecker, Jérôme - Laboratoire de Statistique Théorique et Appliquée, Université Pierre-et-Marie-Curie, Paris 6
ADAPTIVE DENSITY ESTIMATION FOR GENERAL ARCH MODELS , J. DEDECKER2
Conditional convergence to infinitely divisible distributions with finite variance
Inequalities for partial sums of Hilbert valued dependent sequences and applications.
The empirical distribution function for dependent variables: asymptotic and nonasymptotic results in Lp
SOME UNBOUNDED FUNCTIONS OF INTERMITTENT MAPS FOR WHICH THE CENTRAL LIMIT THEOREM HOLDS
New dependence coefficients. Examples and applications to statistics.
Coupling for -dependent sequences and applications
Rates of convergence in the central limit theorem for linear statistics of martingale differences
Weak invariance principle and exponential bounds for some special functions of intermittent maps
On the almost sure invariance principle for stationary sequences of Hilbert-valued random variables
INEGALITES DE HOEFFDING ET THEOR`EME LIMITE CENTRAL POUR DES FONCTIONS PEU REGULI`ERES DE CHA^iNES DE MARKOV NON
Probabilites/Probability Theory, Statistique/Statistics Inegalites de covariance
Rates of convergence for minimal distances in the central limit theorem under projective criteria
Necessary and sufficient conditions for the conditional central limit theorem
On mean central limit theorems for stationary Jer^ome Dedecker1
A central limit theorem for stationary random Jer^ome DEDECKER Universite Paris 11
A new covariance inequality and applications Jer^ome DEDECKER
ESAIM: Probability and Statistics Will be set by the publisher URL: http://www.emath.fr/ps/
An empirical central limit theorem for dependent sequences
Convergence rates in the law of large numbers for Banach valued dependent variables.
Moderate deviations for stationary sequences of bounded random variables
AN EMPIRICAL CENTRAL LIMIT THEOREM FOR INTERMITTENT MAPS J. DEDECKER1
Some almost sure results for unbounded functions of intermittent maps and their associated Markov chains
Maximal Inequalities and Empirical Central Limit
Parametrized Kantorovich-Rubinstein theorem and application to the coupling of random variables.
Invariance principles for linear processes. Application to isotonic regression
On the weak invariance principle for non adapted sequences under projective criteria.
On the functional central limit theorem for stationary processes
Exponential inequalities and functional central limit theorems for random fields
The conditional central limit theorem in Hilbert spaces Jer^ome DEDECKER and Florence MERLEV`EDE
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