000			03982nam a22004695i 4500
001			978-0-387-69952-3
003			DE-He213
005			20250710084010.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387699523 _a99780387699523
024	7		_a10.1007/978-0-387-69952-3 _2doi
082	0	4	_a519.5 _223
100	1		_aDedecker, Jérôme. _eauthor.
245	1	0	_aWeak Dependence: With Examples and Applications _h[recurso electrónico] / _cby Jérôme Dedecker, Paul Doukhan, Gabriel Lang, León R. José Rafael, Sana Louhichi, Clémentine Prieur.
264		1	_aNew York, NY : _bSpringer New York, _c2007.
300			_aXIV, 318 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Statistics, _x0930-0325 ; _v190
505	0		_aWeak dependence -- Models -- Tools for non causal cases -- Tools for causal cases -- Applications of strong laws of large numbers -- Central Limit theorem -- Donsker Principles -- Law of the iterated logarithm (LIL) -- The Empirical process -- Functional estimation -- Spectral estimation -- Econometric applications and resampling.
520			_aThis 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, non-Markovian, 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 Latino-America) is professor at University Central of Venezuela and Paul Doukhan is professor at ENSAE (SAMOS-CES 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.
650		0	_aSTATISTICS.
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
700	1		_aDoukhan, Paul. _eauthor.
700	1		_aLang, Gabriel. _eauthor.
700	1		_aJosé Rafael, León R. _eauthor.
700	1		_aLouhichi, Sana. _eauthor.
700	1		_aPrieur, Clémentine. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387699516
830		0	_aLecture Notes in Statistics, _x0930-0325 ; _v190
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-69952-3 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58150 _d58150