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

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  • Jose M. 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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Bibliographic Info

Paper provided by Universidad Carlos III, Departamento de Economía de la Empresa in its series Business Economics Working Papers with number wb072606.

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Date of creation: May 2007
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Handle: RePEc:cte:wbrepe:wb072606

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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 M. Vidal-Sanz, 2007. "Automatic spectral density estimation for Random fields on a lattice via bootstrap," Business Economics Working Papers wb072606, Universidad Carlos III, Departamento de Economía de la Empresa.

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