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Surface estimation under local stationarity

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  • Sucharita Ghosh

Abstract

Consider a nonparametric regression model involving spatial observations that are nonlinear transformations of a latent Gaussian random field. We address estimation of the variance of the Priestley-Chao kernel estimator of the surface by using a local stationarity-type property which is a result of the assumed transformation. It turns out that it is possible to avoid estimation of the various nuisance parameters so as to estimate the leading term of the asymptotic variance of the estimator. We also address uniform convergence of the nonparametric surface estimator, under short-memory and long-memory correlations in the data.

Suggested Citation

  • Sucharita Ghosh, 2015. "Surface estimation under local stationarity," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(2), pages 229-240, June.
    • Handle: RePEc:taf:gnstxx:v:27:y:2015:i:2:p:229-240
    DOI: 10.1080/10485252.2015.1029473
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