A class of negatively fractal dimensional Gaussian random functions.
Li, Ming (2011)
Mathematical Problems in Engineering
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Displaying similar documents to “On the predictability of long-range dependent series.”
A class of negatively fractal dimensional Gaussian random functions.
Whitening of background brain activity via parametric modeling.
Kamel, Nidal, Samraj, Andrews, Mousavi, Arash (2007)
Discrete Dynamics in Nature and Society
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Nonlinear time series: computations and applications.
Li, Ming, Scalia, Massimo, Toma, Cristian (2010)
Mathematical Problems in Engineering
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Spatial modelling and the statistical analysis of spatial data in human geography
Robert Haining (1987)
Mathématiques et Sciences Humaines
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Variance bound of ACF estimation of one block of fGn with LRD.
Li, Ming, Zhao, Wei (2010)
Mathematical Problems in Engineering
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Crossings of second-order response processes subjected to LMA loadings.
Galtier, Thomas, Gupta, Sayan, Rychlik, Igor (2010)
Journal of Probability and Statistics
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A new adaptive local linear prediction method and its application in hydrological time series.
She, Dunxian, Yang, Xiaohua (2010)
Mathematical Problems in Engineering
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Long memory from Sauerbrey equation: a case in coated quartz crystal microbalance in terms of ammonia.
Wang, Xiaohua, Li, Ming, Chen, Shengyong (2011)
Mathematical Problems in Engineering
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mBm-based scalings of traffic propagated in internet.
Li, Ming, Zhao, Wei, Chen, Shengyong (2011)
Mathematical Problems in Engineering
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High-rate wireless data communications: An underwater acoustic communications framework at the physical layer.
Bessios, Anthony G., Caimi, Frank M. (1996)
Mathematical Problems in Engineering
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Development of the kriging method with application
Pavel Krejčíř (2002)
Applications of Mathematics
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This paper describes a modification of the kriging method for working with the square root transformation of a spatial random process. We have developed this method for the situation where the spatial process observed is not supposed to be stationary but the assumption is that its square root is a second order stationary spatial random process. Consequently this method is developed for estimating the integral of the process observed and finally some application of the method is given...