Optimal sampling for spatial prediction of functional data - Statistical Methods & Applications

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2015
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Abstract
This paper combines optimal spatial sampling designs with geostatistical analysis of functional data. We propose a methodology and design criteria to find the set of spatial locations that minimizes the variance of the spatial functional prediction at unsampled sites for three functional predictors: ordinary kriging, simple kriging and simple cokriging. The last one is a modification of an existing predictor that uses ordinary cokriging based on the basis coefficients. Instead, we propose to use a simple cokriging predictor with the scores resulting from a representation of the functional data with the empirical functional principal components, allowing to remove restrictions and complexity of the covariance models and constraints on the estimation procedure. The methodology is applied to a network of air quality in Bogotá city, Colombia.
Reference Key
bohorquez2015statisticaloptimal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Martha Bohorquez;Ramón Giraldo;Jorge Mateu;Martha Bohorquez;Ramón Giraldo;Jorge Mateu;
Journal Statistical Methods & Applications
Year 2015
DOI doi:10.1007/s10260-015-0340-9
URL
https://dx.doi.org/10.1007/s10260-015-0340-9
Keywords
statistics economics medicine physics Humanities management law Health Sciences Insurance statistics for engineering computer science chemistry and earth sciences general statistical theory and methods statistics for life sciences statistics for social sciences statistics for business finance

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