Doukhan, Paul
Overview
Works:  59 works in 169 publications in 2 languages and 2,052 library holdings 

Roles:  Author, Editor, Thesis advisor 
Classifications:  QA273.18, 519.53 
Publication Timeline
.
Most widely held works by
Paul Doukhan
Dependence in probability and statistics by
Paul Doukhan(
Book
)
38 editions published between 2006 and 2010 in English and held by 383 WorldCat member libraries worldwide
This volume collects recent works on weakly dependent, longmemory and multifractal processes and introduces new dependence measures for studying complex stochastic systems. Other topics include the statistical theory for bootstrap and permutation statistics for infinite variance processes, the dependence structure of maxstable processes, and the statistical properties of spectral estimators of the long memory parameter. The asymptotic behavior of Fejer graph integrals and their use for proving central limit theorems for tapered estimators are investigated. New multifractal processes are intr
38 editions published between 2006 and 2010 in English and held by 383 WorldCat member libraries worldwide
This volume collects recent works on weakly dependent, longmemory and multifractal processes and introduces new dependence measures for studying complex stochastic systems. Other topics include the statistical theory for bootstrap and permutation statistics for infinite variance processes, the dependence structure of maxstable processes, and the statistical properties of spectral estimators of the long memory parameter. The asymptotic behavior of Fejer graph integrals and their use for proving central limit theorems for tapered estimators are investigated. New multifractal processes are intr
Mixing : properties and examples by
Paul Doukhan(
Book
)
17 editions published between 1991 and 1995 in English and held by 263 WorldCat member libraries worldwide
Mixing is concerned with the analysis of dependence between sigmafields defined on the same underlying probability space. It provides an important tool of analysis for random fields, Markov processes, central limit theorems as well as being a topic of current research interest in its own right. The aim of this monograph is to provide a study of applications of dependence in probability and statistics. It is divided in two parts, the first covering the definitions and probabilistic properties of mixing theory. The second part describes mixing properties of classical processes and random fields as well as providing a detailed study of linear and Gaussian fields. Consequently, this book will provide statisticians dealing with problems involving weak dependence properties with a powerful tool
17 editions published between 1991 and 1995 in English and held by 263 WorldCat member libraries worldwide
Mixing is concerned with the analysis of dependence between sigmafields defined on the same underlying probability space. It provides an important tool of analysis for random fields, Markov processes, central limit theorems as well as being a topic of current research interest in its own right. The aim of this monograph is to provide a study of applications of dependence in probability and statistics. It is divided in two parts, the first covering the definitions and probabilistic properties of mixing theory. The second part describes mixing properties of classical processes and random fields as well as providing a detailed study of linear and Gaussian fields. Consequently, this book will provide statisticians dealing with problems involving weak dependence properties with a powerful tool
Theory and applications of longrange dependence(
Book
)
9 editions published in 2003 in English and held by 256 WorldCat member libraries worldwide
9 editions published in 2003 in English and held by 256 WorldCat member libraries worldwide
Cours d'analyse : analyse réelle et intégration : agrégation de mathématiques by
Paul Doukhan(
Book
)
3 editions published in 2001 in French and held by 78 WorldCat member libraries worldwide
3 editions published in 2001 in French and held by 78 WorldCat member libraries worldwide
Cours d'analyse. agrégation de mathématiques by
Paul Doukhan(
Book
)
2 editions published in 2002 in French and held by 64 WorldCat member libraries worldwide
2 editions published in 2002 in French and held by 64 WorldCat member libraries worldwide
Weak dependence : with examples and applications by
Jérôme Dedecker(
Book
)
6 editions published in 2007 in English and held by 24 WorldCat member libraries worldwide
"This monograph is aimed at developing Doukhan/Louhichi's (1999) idea to measure asymptotic independence of a random process. The authors propose various examples of models fitting such conditions such as stable Markov chains, dynamical systems or more complicated models, nonlinear, nonMarkovian, and heteroskedastic models with infinite memory. Most of the commonly used stationary models fit their conditions. The simplicity of the conditions is also their strength. The main existing tools for an asymptotic theory are developed under weak dependence. They apply the theory to nonparametric statistics, spectral analysis, econometrics, and resampling. The level of generality makes those techniques quite robust with respect to the model. The limit theorems are sometimes sharp and always simple to apply. The theory (with proofs) is developed and the authors propose to fix the notation for future applications. A large number of research papers deals with the present ideas; the authors as well as numerous other investigators participated actively in the development of this theory. Several applications are still needed to develop a method of analysis for (nonlinear) times series and they provide here a strong basis for such studies. Jérôme Dedecker (associate professor Paris 6), Gabriel Lang (professor at Ecole Polytechnique, ENGREF Paris), Sana Louhichi (Paris 11, associate professor at Paris 2), and Clémentine Prieur (associate professor at INSA, Toulouse) are main contributors for the development of weak dependence. José Rafael León (Polar price, correspondent of the Bernoulli society for LatinoAmerica) is professor at University Central of Venezuela and Paul Doukhan is professor at ENSAE (SAMOSCES Paris 1 and Cergy Pontoise) and associate editor of Stochastic Processes and their Applications. His Mixing: Properties and Examples (Springer, 1994) is a main reference for the concurrent notion of mixing."  Font no determinada
6 editions published in 2007 in English and held by 24 WorldCat member libraries worldwide
"This monograph is aimed at developing Doukhan/Louhichi's (1999) idea to measure asymptotic independence of a random process. The authors propose various examples of models fitting such conditions such as stable Markov chains, dynamical systems or more complicated models, nonlinear, nonMarkovian, and heteroskedastic models with infinite memory. Most of the commonly used stationary models fit their conditions. The simplicity of the conditions is also their strength. The main existing tools for an asymptotic theory are developed under weak dependence. They apply the theory to nonparametric statistics, spectral analysis, econometrics, and resampling. The level of generality makes those techniques quite robust with respect to the model. The limit theorems are sometimes sharp and always simple to apply. The theory (with proofs) is developed and the authors propose to fix the notation for future applications. A large number of research papers deals with the present ideas; the authors as well as numerous other investigators participated actively in the development of this theory. Several applications are still needed to develop a method of analysis for (nonlinear) times series and they provide here a strong basis for such studies. Jérôme Dedecker (associate professor Paris 6), Gabriel Lang (professor at Ecole Polytechnique, ENGREF Paris), Sana Louhichi (Paris 11, associate professor at Paris 2), and Clémentine Prieur (associate professor at INSA, Toulouse) are main contributors for the development of weak dependence. José Rafael León (Polar price, correspondent of the Bernoulli society for LatinoAmerica) is professor at University Central of Venezuela and Paul Doukhan is professor at ENSAE (SAMOSCES Paris 1 and Cergy Pontoise) and associate editor of Stochastic Processes and their Applications. His Mixing: Properties and Examples (Springer, 1994) is a main reference for the concurrent notion of mixing."  Font no determinada
Étude de processus melangeant by
Paul Doukhan(
Book
)
6 editions published in 1986 in French and held by 10 WorldCat member libraries worldwide
We study here asymptomatic properties of mixing processes and their statistical applications. First we give sufficient conditions for mixing of classical processes like nonlinear autoregressive ones. After that we give fundamental moment inequalities for sums and for cumulants sums; they allow us to obtain good versions of central limit theorem giving rates with respect to Dudley, Levy of Prohorov metrics. With these tools we give a weak invariance principle for the empirical multidimensional repartition function with arithmetic rate of convergence. We also give rates of convergence in the weak invariance principle for empirical measure in Sobolev spaces and for kernel estimates of the density and of the regression of mixing sequences. From another hand we give asymptotically Gaussian results for quadratic deviation of non parametric estimates from a various kind. Finally we give invariance principles and functional law of the iterated logarithm in the cases of the local time of a Markov recurrent process and of the empirical spectral density of a stationary mixing random process
6 editions published in 1986 in French and held by 10 WorldCat member libraries worldwide
We study here asymptomatic properties of mixing processes and their statistical applications. First we give sufficient conditions for mixing of classical processes like nonlinear autoregressive ones. After that we give fundamental moment inequalities for sums and for cumulants sums; they allow us to obtain good versions of central limit theorem giving rates with respect to Dudley, Levy of Prohorov metrics. With these tools we give a weak invariance principle for the empirical multidimensional repartition function with arithmetic rate of convergence. We also give rates of convergence in the weak invariance principle for empirical measure in Sobolev spaces and for kernel estimates of the density and of the regression of mixing sequences. From another hand we give asymptotically Gaussian results for quadratic deviation of non parametric estimates from a various kind. Finally we give invariance principles and functional law of the iterated logarithm in the cases of the local time of a Markov recurrent process and of the empirical spectral density of a stationary mixing random process
Non linear ARXmodels : probabilistic properties and consistent recursive estimation by
Paul Doukhan(
Book
)
5 editions published in 1992 in English and held by 9 WorldCat member libraries worldwide
5 editions published in 1992 in English and held by 9 WorldCat member libraries worldwide
Etude des processus X indice N = F (X indice N 1) + epsilon indice N by
Paul Doukhan(
Book
)
4 editions published in 1980 in French and held by 6 WorldCat member libraries worldwide
4 editions published in 1980 in French and held by 6 WorldCat member libraries worldwide
Longrange dependence : theory and applications(
Book
)
1 edition published in 2002 in English and held by 5 WorldCat member libraries worldwide
1 edition published in 2002 in English and held by 5 WorldCat member libraries worldwide
Etude probabiliste de la chaine de markov : X indice (N + 1) = F (X indice N) * epsilon indice N by
Paul Doukhan(
Book
)
3 editions published in 1980 in French and held by 4 WorldCat member libraries worldwide
3 editions published in 1980 in French and held by 4 WorldCat member libraries worldwide
Spectral estimation for a strongly dependent stationary gaussian field by
Paul Doukhan(
Book
)
2 editions published in 1991 in English and held by 4 WorldCat member libraries worldwide
2 editions published in 1991 in English and held by 4 WorldCat member libraries worldwide
Estimation de la transition de probabilite d'une chaine de markov doeblinrecurrente; etude du cas particulier du processus
autoregressif general d'ordre 1 by
Paul Doukhan(
Book
)
3 editions published in 1980 in French and held by 4 WorldCat member libraries worldwide
3 editions published in 1980 in French and held by 4 WorldCat member libraries worldwide
Simulations dans le processus autoregressif general d'ordre 1; cas unidimensionnel by
Paul Doukhan(
Book
)
3 editions published in 1981 in French and held by 4 WorldCat member libraries worldwide
3 editions published in 1981 in French and held by 4 WorldCat member libraries worldwide
ETUDE DES PROCESSUS XN=F(XN1)+EPSILON : :(N) by
Paul Doukhan(
Book
)
2 editions published in 1980 in French and held by 3 WorldCat member libraries worldwide
CETTE THESE PROPOSE UNE ETUDE EN DEUX PARTIES L'UNE PROBABILISTE ET L'AUTRE STATISTIQUE DU PROCESSUS AUTOREGRESSIF GENERAL D'ORDRE UN
2 editions published in 1980 in French and held by 3 WorldCat member libraries worldwide
CETTE THESE PROPOSE UNE ETUDE EN DEUX PARTIES L'UNE PROBABILISTE ET L'AUTRE STATISTIQUE DU PROCESSUS AUTOREGRESSIF GENERAL D'ORDRE UN
Estimation des fonctionnelles de la densite spectrale des processus gaussiens dans differents cadres de dependance by
Carenne Ludeña(
Book
)
1 edition published in 1996 in French and held by 3 WorldCat member libraries worldwide
NOUS ABORDONS CERTAINS PROBLEMES STATISTIQUES LIES A L'ESTIMATION, SUR LA BASE DES OBSERVATIONS SUR UN PAVE RECTANGULAIRE DISCRET MULTIDIMENSIONNEL D'UN PROCESSUS CENTRE, STATIONNAIRE ET GAUSSIEN, DES FONCTIONNELLES DE LA DENSITE SPECTRALE DANS DIFFERENTS CADRES DE DEPENDANCE. DANS UNE PREMIERE PARTIE NOUS NOUS CONSACRONS A L'ESTIMATION EFFICACE DES FONCTIONNELLES LINEAIRES ET NON LINEAIRES FRECHET DIFFERENTIABLES. ON SUPPOSE QUE LA DENSITE APPARTIENT A UNE CERTAINE CLASSE DES FONCTIONS EQUICONTINUES DANS L'ENSEMBLE DES FONCTIONS DE CARRE INTEGRABLE. EN UTILISANT LA THEORIE DES FAMILLES D'ESTIMATEURS LAN ON TROUVE DES BORNES INFERIEURES DANS UN SENS MINIMAX LOCAL. ON CONSTRUIT ENSUITE DES ESTIMATEURS EFFICACES EN UTILISANT UN DEVELOPPEMENT EN SERIE DE TAYLOR DE PREMIER ORDRE DE LA FONCTIONNELLE DANS LES CAS D=2 ET D=3. DANS LE CAS D=1 ON CONSIDERE AUSSI DES DEVELOPPEMENTS D'ORDRE DEUX DE LA FONCTIONNELLE, CONSTRUITS SUR LA BASE DE L'INTEGRALE DU PERIODOGRAMME AU CARRE. DANS UNE DEUXIEME PARTIE NOUS ETUDIONS CERTAINS ESTIMATEURS PARAMETRIQUES DANS UN CADRE DE DEPENDANCE FORTE. AINSI, ON GENERALISE AU CAS DE CHAMPS (D=2, D=3) CERTAINS RESULTATS EXISTANTS CONCERNANT LES PROPRIETES ASYMPTOTIQUES DE L'ESTIMATEUR PAR MINIMISATION DE LA QUASI VRAISEMBLANCE, DIT DE WHITTLE, POUR LES PROCESSUS STATIONNAIRES GAUSSIENS (D=1). POUR CONTROLER LE BIAIS ON EST OBLIGE D'INTRODUIRE UNE FONCTION DE REGULARISATION, POUR COMPENSER LA SINGULARITE EN ZERO DE LA DENSITE SPECTRALE. ON CONSIDERE AUSSI UN AUTRE SCHEMA D'ESTIMATION INTRODUIT PAR TANIGUCHI. POUR CELA ON ETUDIE DES DISCRETISATIONS DES INTEGRALES DU LOGARITHME DU PERIODOGRAMME. L'ETUDE DU COMPORTEMENT ASYMPTOTIQUE DE CES INTEGRALES DISCRETISES A ETE FAIT EN UTILISANT UN DEVELOPPEMENT SUR LA BASE DES POLYNOMES D'HERMITE DU PERIODOGRAMME. CETTE METHODE D'ESTIMATION PEUT ETRE CONSIDEREE COMME UNE VERSION PARAMETRIQUE DE L'ESTIMATEUR SEMIPARAMETRIQUE PROPOSE PAR GEWEKE ET PORTERHUDAK
1 edition published in 1996 in French and held by 3 WorldCat member libraries worldwide
NOUS ABORDONS CERTAINS PROBLEMES STATISTIQUES LIES A L'ESTIMATION, SUR LA BASE DES OBSERVATIONS SUR UN PAVE RECTANGULAIRE DISCRET MULTIDIMENSIONNEL D'UN PROCESSUS CENTRE, STATIONNAIRE ET GAUSSIEN, DES FONCTIONNELLES DE LA DENSITE SPECTRALE DANS DIFFERENTS CADRES DE DEPENDANCE. DANS UNE PREMIERE PARTIE NOUS NOUS CONSACRONS A L'ESTIMATION EFFICACE DES FONCTIONNELLES LINEAIRES ET NON LINEAIRES FRECHET DIFFERENTIABLES. ON SUPPOSE QUE LA DENSITE APPARTIENT A UNE CERTAINE CLASSE DES FONCTIONS EQUICONTINUES DANS L'ENSEMBLE DES FONCTIONS DE CARRE INTEGRABLE. EN UTILISANT LA THEORIE DES FAMILLES D'ESTIMATEURS LAN ON TROUVE DES BORNES INFERIEURES DANS UN SENS MINIMAX LOCAL. ON CONSTRUIT ENSUITE DES ESTIMATEURS EFFICACES EN UTILISANT UN DEVELOPPEMENT EN SERIE DE TAYLOR DE PREMIER ORDRE DE LA FONCTIONNELLE DANS LES CAS D=2 ET D=3. DANS LE CAS D=1 ON CONSIDERE AUSSI DES DEVELOPPEMENTS D'ORDRE DEUX DE LA FONCTIONNELLE, CONSTRUITS SUR LA BASE DE L'INTEGRALE DU PERIODOGRAMME AU CARRE. DANS UNE DEUXIEME PARTIE NOUS ETUDIONS CERTAINS ESTIMATEURS PARAMETRIQUES DANS UN CADRE DE DEPENDANCE FORTE. AINSI, ON GENERALISE AU CAS DE CHAMPS (D=2, D=3) CERTAINS RESULTATS EXISTANTS CONCERNANT LES PROPRIETES ASYMPTOTIQUES DE L'ESTIMATEUR PAR MINIMISATION DE LA QUASI VRAISEMBLANCE, DIT DE WHITTLE, POUR LES PROCESSUS STATIONNAIRES GAUSSIENS (D=1). POUR CONTROLER LE BIAIS ON EST OBLIGE D'INTRODUIRE UNE FONCTION DE REGULARISATION, POUR COMPENSER LA SINGULARITE EN ZERO DE LA DENSITE SPECTRALE. ON CONSIDERE AUSSI UN AUTRE SCHEMA D'ESTIMATION INTRODUIT PAR TANIGUCHI. POUR CELA ON ETUDIE DES DISCRETISATIONS DES INTEGRALES DU LOGARITHME DU PERIODOGRAMME. L'ETUDE DU COMPORTEMENT ASYMPTOTIQUE DE CES INTEGRALES DISCRETISES A ETE FAIT EN UTILISANT UN DEVELOPPEMENT SUR LA BASE DES POLYNOMES D'HERMITE DU PERIODOGRAMME. CETTE METHODE D'ESTIMATION PEUT ETRE CONSIDEREE COMME UNE VERSION PARAMETRIQUE DE L'ESTIMATEUR SEMIPARAMETRIQUE PROPOSE PAR GEWEKE ET PORTERHUDAK
Principes d'invariance faibles, avec vitesse, dans un cadre melangeant by
Paul Doukhan(
Book
)
2 editions published in 1983 in French and held by 3 WorldCat member libraries worldwide
2 editions published in 1983 in French and held by 3 WorldCat member libraries worldwide
Invariance principles for absolutely regular empirical processes by
Paul Doukhan(
Book
)
2 editions published in 1992 in English and held by 3 WorldCat member libraries worldwide
2 editions published in 1992 in English and held by 3 WorldCat member libraries worldwide
Theoremes limites pour des suites positivement ou faiblement dependantes by
Sana Louhichi(
Book
)
1 edition published in 1998 in French and held by 3 WorldCat member libraries worldwide
CETTE THESE EST COMPOSEE DE DEUX PARTIES. LA PREMIERE PARTIE EST CONSACREE A L'ETUDE DES PROPRIETES ASYMPTOTIQUES DES SUITES VERIFIANT UNE CONDITION DE DEPENDANCE POSITIVE DITE ASSOCIATION, CONDITION CONNUE EN MECANIQUE STATISTIQUE SOUS LE NOM DE INEGALITE FKG. DANS UN PREMIER TEMPS, NOUS MONTRONS DES VERSIONS DE L'INEGALITE DE ROSENTHAL POUR DES SUITES QUI SONT LINEAIREMENT POSITIVEMENT QUADRANT DEPENDANTES, GENERALISANT AINSI L'INEGALITE DEMONTREE DANS LE CADRE DE L'INDEPENDANCE. NOUS AVONS MONTRE, ENSUITE, MARCINKIEWICZZYGMUND LOI FORTE DE GRANDS NOMBRES POUR DES SUITES ASSOCIEES SANS MOMENTS D'ORDRE DEUX. NOS HYPOTHESES PORTENT SUR UNE DECROISSANCE DES COVARIANCES DES VARIABLES ADEQUATEMENT TRONQUEES ET SUR UNE CONDITION MINIMALE SUR LES MOMENTS DES VARIABLES. NOUS APPLIQUONS, ENSUITE, NOS RESULTATS AUX SUITES LINEAIRES, A INNOVATIONS STABLES. NOUS NOUS SOMMES INTERESSES AUSSI AUX VITESSES DE CONVERGENCE DANS LE THEOREME DE LA LIMITE CENTRALE SOUS UNE DECROISSANCE HYPERBOLIQUE DES COVARIANCES. NOUS ADAPTONS LA METHODE DE LINDEBERG UTILISEE PAR RIO DANS LE CAS DU MELANGE. NOUS DONNONS AINSI DES ORDRES DE GRANDEUR DANS LE THEOREME DE BERRYESSEEN POUR DES SUITES VERIFIANT UNE PROPRIETE DE DEPENDANCE PLUS GENERALE QUE LA DEPENDANCE POSITIVE. NOUS DONNONS AUSSI DES EVALUATIONS DE LA DISTANCE DE DUDLEY POUR CES SUITES. DES INEGALITES DE ROSENTHAL SONT AUSSI ECRITES. NOUS MONTRONS, ENFIN, UN THEOREME DE LA LIMITE CENTRALE EMPIRIQUE EN ASSOCIATION AMELIORANT AINSI UN RESULTAT RECENT A SHAO ET YU. LA DEUXIEME PARTIE A POUR OBJECTIF ESSENTIEL DE DEFINIR UNE DEPENDANCE FAIBLE PLUS GENERALE QUE LE MELANGE ET L'ASSOCIATION. SOUS CETTE DEFINITION, NOUS MONTRONS DES INEGALITES DE MOMENTS ET NOUS PROPOSONS DES DIVERSES APPLICATIONS EN PROBABILITE ET DANS LE DOMAINE DE L'ESTIMATION NONPARAMETRIQUE
1 edition published in 1998 in French and held by 3 WorldCat member libraries worldwide
CETTE THESE EST COMPOSEE DE DEUX PARTIES. LA PREMIERE PARTIE EST CONSACREE A L'ETUDE DES PROPRIETES ASYMPTOTIQUES DES SUITES VERIFIANT UNE CONDITION DE DEPENDANCE POSITIVE DITE ASSOCIATION, CONDITION CONNUE EN MECANIQUE STATISTIQUE SOUS LE NOM DE INEGALITE FKG. DANS UN PREMIER TEMPS, NOUS MONTRONS DES VERSIONS DE L'INEGALITE DE ROSENTHAL POUR DES SUITES QUI SONT LINEAIREMENT POSITIVEMENT QUADRANT DEPENDANTES, GENERALISANT AINSI L'INEGALITE DEMONTREE DANS LE CADRE DE L'INDEPENDANCE. NOUS AVONS MONTRE, ENSUITE, MARCINKIEWICZZYGMUND LOI FORTE DE GRANDS NOMBRES POUR DES SUITES ASSOCIEES SANS MOMENTS D'ORDRE DEUX. NOS HYPOTHESES PORTENT SUR UNE DECROISSANCE DES COVARIANCES DES VARIABLES ADEQUATEMENT TRONQUEES ET SUR UNE CONDITION MINIMALE SUR LES MOMENTS DES VARIABLES. NOUS APPLIQUONS, ENSUITE, NOS RESULTATS AUX SUITES LINEAIRES, A INNOVATIONS STABLES. NOUS NOUS SOMMES INTERESSES AUSSI AUX VITESSES DE CONVERGENCE DANS LE THEOREME DE LA LIMITE CENTRALE SOUS UNE DECROISSANCE HYPERBOLIQUE DES COVARIANCES. NOUS ADAPTONS LA METHODE DE LINDEBERG UTILISEE PAR RIO DANS LE CAS DU MELANGE. NOUS DONNONS AINSI DES ORDRES DE GRANDEUR DANS LE THEOREME DE BERRYESSEEN POUR DES SUITES VERIFIANT UNE PROPRIETE DE DEPENDANCE PLUS GENERALE QUE LA DEPENDANCE POSITIVE. NOUS DONNONS AUSSI DES EVALUATIONS DE LA DISTANCE DE DUDLEY POUR CES SUITES. DES INEGALITES DE ROSENTHAL SONT AUSSI ECRITES. NOUS MONTRONS, ENFIN, UN THEOREME DE LA LIMITE CENTRALE EMPIRIQUE EN ASSOCIATION AMELIORANT AINSI UN RESULTAT RECENT A SHAO ET YU. LA DEUXIEME PARTIE A POUR OBJECTIF ESSENTIEL DE DEFINIR UNE DEPENDANCE FAIBLE PLUS GENERALE QUE LE MELANGE ET L'ASSOCIATION. SOUS CETTE DEFINITION, NOUS MONTRONS DES INEGALITES DE MOMENTS ET NOUS PROPOSONS DES DIVERSES APPLICATIONS EN PROBABILITE ET DANS LE DOMAINE DE L'ESTIMATION NONPARAMETRIQUE
Dependence in Probability and Statistics. Lecture Notes in Statistics, Volume 187(
)
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
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
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
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
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Related Identities
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 Oppenheim, Georges Editor
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 Lang, Gabriel
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 Sifre, JeanClaude
 Surgailis, Donatas
 Teyssière, Gilles
Associated Subjects
Brownian motion processes Dependence (Statistics) Distribution (Probability theory) Limit theorems (Probability theory) Mathematical analysis Mathematical statistics Mathematics Mixture distributions (Probability theory) Nonparametric statisticsAsymptotic theory Probabilities Random variables Statistics Stochastic processes Timeseries analysis