A note on decoupling of local and global behaviours for the Dagum Random Field

Abstract Self-affinity versus decoupling: this dichotomy represents a breakthrough with respect to the previous literature, that has grown under the dogma of self-affinity. The word decoupling refers to those correlation functions allowing to treat independently the Hausdorff–Besicovitch dimension and Hurst effect parameters. The former is a roughness measure associated to profiles or surfaces. The latter reflects possible persistent or antipersistent behaviours of the associated random process or random field. Thus, the decoupling philosophy opens new avenues for the analysis and interpretation of local and global properties of random fields. In this paper, we introduce a new class of isotropic correlation functions, called Dagum, show its permissibility on any n -dimensional space, and analyse its attitudes with respect to decoupling. Interesting aspects arise from an intensive simulation study, conducted in one and two dimensions. In particular, it seems that the decoupling attitude may depend on the space dimension.

[2]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[3]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Ignacio N. Lobato,et al.  Averaged periodogram estimation of long memory , 1996 .

[5]  C.-C. Jay Kuo,et al.  Extending self-similarity for fractional Brownian motion , 1994, IEEE Trans. Signal Process..

[6]  Tilmann Gneiting,et al.  Stochastic Models That Separate Fractal Dimension and the Hurst Effect , 2001, SIAM Rev..

[7]  George Christakos,et al.  Modern Spatiotemporal Geostatistics , 2000 .

[8]  P. Robinson Semiparametric Analysis of Long-Memory Time Series , 1994 .

[9]  Near Wall Turbulence Modeling Using Fractal Dimensions , 1999 .

[10]  M. Ostoja-Starzewski,et al.  Linear elasticity of planar delaunay networks: Random field characterization of effective moduli , 1989 .

[11]  R. Askey Theory and application of special functions : proceedings of an advanced seminar sponsored by the Mathematics Research Center, the University of Wisconsin-Madison, March 31-April 2, 1975 , 1975 .

[12]  Martin Ostoja-Starzewski,et al.  Random field models of heterogeneous materials , 1998 .

[13]  A. Yaglom Correlation Theory of Stationary and Related Random Functions I: Basic Results , 1987 .

[14]  P. Robinson Gaussian Semiparametric Estimation of Long Range Dependence , 1995 .

[15]  S. Bochner Monotone Funktionen, Stieltjessche Integrale und harmonische Analyse , 1933 .

[16]  Ove Ditlevsen,et al.  A story about estimation of a random field of boulders from incomplete seismic measurements , 2005 .

[17]  M. Ostoja-Starzewski Material spatial randomness: From statistical to representative volume element☆ , 2006 .

[18]  Peter Whittle,et al.  Hypothesis Testing in Time Series Analysis. , 1951 .

[19]  S. P. Neuman,et al.  Generating and scaling fractional Brownian motion on finite domains , 2005 .

[20]  Andrew T. A. Wood,et al.  On the performance of box-counting estimators of fractal dimension , 1993 .

[21]  W. Willinger,et al.  ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY , 1995 .

[22]  P. Hall,et al.  Characterizing surface smoothness via estimation of effective fractal dimension , 1994 .

[23]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[24]  B. Mandelbrot,et al.  Fractional Brownian Motions, Fractional Noises and Applications , 1968 .

[25]  A. Lo Long-Term Memory in Stock Market Prices , 1989 .

[26]  G. Christakos On the Problem of Permissible Covariance and Variogram Models , 1984 .

[27]  Christian Berg,et al.  Potential Theory on Locally Compact Abelian Groups , 1975 .

[28]  P. Robinson Log-Periodogram Regression of Time Series with Long Range Dependence , 1995 .

[29]  Scotti,et al.  Fractal dimension of velocity signals in high-Reynolds-number hydrodynamic turbulence. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[30]  Manabu Tanaka,et al.  ESTIMATION OF THE FRACTAL DIMENSION OF FRACTURE SURFACE PATTERNS BY BOX-COUNTING METHOD , 1999 .

[31]  J. R. Wallis,et al.  Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence , 1969 .

[32]  I. J. Schoenberg Metric spaces and completely monotone functions , 1938 .

[33]  Fallaw Sowell Maximum likelihood estimation of stationary univariate fractionally integrated time series models , 1992 .

[34]  Andrew J. Majda,et al.  A Fourier-Wavelet Monte Carlo Method for Fractal Random Fields , 1997 .

[35]  Jorge Mateu,et al.  Modelling spatio-temporal data: A new variogram and covariance structure proposal , 2007 .

[36]  Marc A. Maes,et al.  Random Field Modeling of Elastic Properties Using Homogenization , 2001 .

[37]  Gareth O. Roberts,et al.  Robust Markov chain Monte Carlo Methods for Spatial Generalized Linear Mixed Models , 2006 .

[38]  T. Gneiting,et al.  Fast and Exact Simulation of Large Gaussian Lattice Systems in ℝ2: Exploring the Limits , 2006 .