Reduced rank covariances for the analysis of environmental data

Orietta Nicolis, Doug Nychka

Resultado de la investigación: Contribución a los tipos de informe/libroCapítulo

Resumen

In this work we propose a Monte Carlo estimator for non stationary covariances of large incomplete lattice or irregularly distributed data. In particular, we propose a method called “reduced rank covariance” (RRC), based on the multiresolution approach for reducing the dimensionality of the spatial covariances. The basic idea is to estimate the covariance on a lower resolution grid starting from a stationary model (such as the Mathérn covariance) and use the multiresolution property of wavelet basis for evaluating the covariance on the full grid. Since this method doesn’t need to compute the wavelet coefficients, it is very fast in estimating covariances in large data sets. The spatial forecasting performances of the method has been described through a simulation study. Finally, the method has been applied to two environmental data sets: the aerosol optical thickness (AOT) satellite data observed in Northern Italy and the ozone concentrations in the eastern United States.

Idioma originalInglés
Título de la publicación alojadaStudies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies
EditorialSpringer International Publishing AG
Páginas253-263
Número de páginas11
DOI
EstadoPublicada - 1 ene 2012

Serie de la publicación

NombreStudies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies
ISSN (versión impresa)2194-7767
ISSN (versión digital)2194-7775

Áreas temáticas de ASJC Scopus

  • Estadística y probabilidad

Huella Profundice en los temas de investigación de 'Reduced rank covariances for the analysis of environmental data'. En conjunto forman una huella única.

  • Citar esto

    Nicolis, O., & Nychka, D. (2012). Reduced rank covariances for the analysis of environmental data. En Studies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies (pp. 253-263). (Studies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies). Springer International Publishing AG. https://doi.org/10.1007/978-3-642-21037-2_23