Titlebook: Long-Memory Processes; Probabilistic Proper Jan Beran,Yuanhua Feng,Rafal Kulik Book 2013 Springer-Verlag Berlin Heidelberg 2013 62Mxx, 62M0

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书目名称Long-Memory Processes影响因子(影响力)




书目名称Long-Memory Processes影响因子(影响力)学科排名




书目名称Long-Memory Processes网络公开度




书目名称Long-Memory Processes网络公开度学科排名




书目名称Long-Memory Processes被引频次




书目名称Long-Memory Processes被引频次学科排名




书目名称Long-Memory Processes年度引用




书目名称Long-Memory Processes年度引用学科排名




书目名称Long-Memory Processes读者反馈




书目名称Long-Memory Processes读者反馈学科排名

神圣不可

Origins and Generation of Long Memory,es. On the other hand, sometimes one prefers to be lead by subject specific considerations. Typical for the first approach is the definition of linear processes with long memory, or fractional ARIMA models. Subject specific models have been developed for instance in physics, finance and network engi

女歌星

Mathematical Concepts,description of univariate orthogonal polynomials in Sect. ., with particular emphasis on Hermite polynomials in Sect. .. Under suitable conditions, a function . can be expanded into a series . with respect to an orthogonal basis consisting of Hermite polynomials ..(⋅) (.). Such expansions are used t

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Statistical Inference for Nonlinear Processes,.3–2.4 and Sect. . for limit theorems), ARCH(∞) processes (see Definition 2.1 and Sect. .) and LARCH(∞) models (see (.) and (.), and Sect. .). Statistical inference for traffic models is not well developed yet (see Faÿ et al. in Queueing Syst. 54(2):121–140, ., Bernoulli 13(2):473–491, .; Hsieh et a

CLAIM

Statistical Inference for Nonstationary Processes,this is of particular interest because long-range dependence often generates sample paths that mimic certain features of nonstationarity. It is therefore often not easy to distinguish between stationary long-memory behaviour and nonstationary structures. For statistical inference, including estimati

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Spatial and Space-Time Processes,. Sometimes observations are obtained on a regular lattice (see, e.g. Whittle in Biometrika 49:305–314, .; Bartlett in Adv. Appl. Prob. 6(2):336–358, .; Besag in J. R. Stat. Soc., Ser. B 36(2):192–236, .; Cressie in Statistics for spatial data, .; Christakos in Random field models in Earth sciences,

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single

durch eine Analogie der Austauschvorgänge erklärt werden kann. In Abbildung 4 ist in dimensionsloser Form der Geschwindigkeits–, Konzentrations- und Temperaturverlauf im Anfangsbereich aufgetragen. Auf der Abszisse ist aufgetragen das Verhältnis der Abstände zwischen 1eßpunkt und dem Punkt halber M

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