Skip to main content

Model-based inference for k-nearest neighbours predictions using a canonical vine copula

Buy Article:

$55.00 plus tax (Refund Policy)

The k-near neighbours (k-NN) technique combines field data from forest inventories and auxiliary information for forest resource estimation at various geographical scales. In this study, auxiliary data consisting of Landsat 5 TM satellite imagery and terrain elevations were used to perform k-NN imputations of plot-level above ground biomass. Following the model-based inference, a superpopulation model consisting of a canonical vine copula was constructed from the empirical data, and new samples were generated from the model and used for k-NN predictions. The method used herein allows constructing the sampling distribution for the imputation errors and for assessing the statistical properties of the k-NN estimator. Using a data-splitting procedure, the copula-based approach was assessed against pair-bootstrap resampling. The imputations were performed using k (the number of neighbours) = 1 and by using optimal k values selected according to a bias-minimizing criterion. The empirical coverage probabilities of the confidence intervals constructed using the copula-based approach were closer to the nominal coverages. The improvements were due to significant bias reduction, while the standard errors were higher compared to the bootstrap. Still, the root mean squared error was significantly reduced. The best results were obtained using the copula approach and k-NN imputations with k=1.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Keywords: copulas; k-NN imputations; variance estimation

Document Type: Research Article

Affiliations: Department of Ecology and Natural Resource Management,Norwegian University of Life Sciences, Ås, Norway

Publication date: 2013-04-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more