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Automatic spectral density estimation for random fields on a lattice via bootstrap

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  • Jose Vidal-Sanz

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Abstract

This paper considers the nonparametric estimation of spectral densities for second order stationary random fields on a d-dimensional lattice. I discuss some drawbacks of standard methods, and propose modified estimator classes with improved bias convergence rate, emphasizing the use of kernel methods and the choice of an optimal smoothing number. I prove uniform consistency and study the uniform asymptotic distribution, when the optimal smoothing number is estimated from the sampled data.

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File URL: http://hdl.handle.net/10.1007/s11749-007-0059-5
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Bibliographic Info

Article provided by Springer in its journal TEST.

Volume (Year): 18 (2009)
Issue (Month): 1 (May)
Pages: 96-114

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Handle: RePEc:spr:testjl:v:18:y:2009:i:1:p:96-114

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Related research

Keywords: Spatial data; Spectral density; Smoothing number; Uniform asymptotic distribution; Bootstrap; 62M30; 62M15; 62G20;

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  1. Jose Vidal-Sanz, 2009. "Automatic spectral density estimation for random fields on a lattice via bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 18(1), pages 96-114, May.
  2. Peter M. Robinson & J. Vidal Sanz, 2005. "Modified whittle estimation of multilateral models on a lattice," LSE Research Online Documents on Economics 4545, London School of Economics and Political Science, LSE Library.
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Cited by:
  1. Jose Vidal-Sanz, 2009. "Automatic spectral density estimation for random fields on a lattice via bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 18(1), pages 96-114, May.

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