Asymptotic properties of LSE of regression coefficients on singular random fields observed on a sphere

We present some upper bounds on the rate of convergence in the central limit theorem for normalized least square estimates (LSE) in a spherical regression model with long range dependence (LRD) stationary errors. The used method is based on the asymptotic analysis of orthogonal expansion of non-line...

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Bibliographic Details
Published in:Chaos, solitons and fractals solitons and fractals, 2003-12, Vol.18 (5), p.1015-1023
Main Author: El-Bassiouny, Ahmed H.
Format: Article
Language:English
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Summary:We present some upper bounds on the rate of convergence in the central limit theorem for normalized least square estimates (LSE) in a spherical regression model with long range dependence (LRD) stationary errors. The used method is based on the asymptotic analysis of orthogonal expansion of non-linear functionals of homogeneous isotropic Gaussian random fields and on the Kolmogorov distance. The theory have many applications in science for instance in evaluating the COBE data.
ISSN:0960-0779
1873-2887
DOI:10.1016/S0960-0779(03)00210-8