Design-based diagnostics for k-NN estimators of forest resources
Author(s)
Baffetta, Federica
Corona, Piermaria
Fattorini, Lorenzo
Date Issued
2011
Type
Article
Abstract
The k-nearest neighbours (k-NN) method constitutes a possible approach to improve the precision of the Horvitz–
Thompson estimator of a single interest variable using auxiliary information at the estimation stage. Improvements
are likely to occur when the neighbouring structure in the space of auxiliary variables is similar to the neighbouring structure
in the space of the survey variables. Populations suitable for k-NN can be identified via the scores of the first principal
component computed on the variance–covariance matrix of auxiliary variables. If the first principal component
explains a large portion of the whole variability, distances among scores provide good approximations of distances in the
space of auxiliary variables in such a way that the effectiveness of k-NN can be assessed by plotting the first principal
component scores versus the sampled values of each of the interest variables. Monotone relationships with high values of
Spearman’s correlation coefficients should denote effectiveness. Otherwise, when the first principal component explains
small fractions of the total variation, an index that directly quantifies the similarity between the neighbouring structure in
the space of interest and auxiliary variables is proposed. The validity of the proposed diagnostics is theoretically argued
and empirically proven by a simulation study performed on a wide range of artificial and real populations.
Additional information
L'articolo è disponibile sul sito dell'editore www.nrcresearchpress.com
Citation
Baffetta, F., Corona, P., Fattorini, L. 2011. Design-based diagnostics for k-NN estimators of forest resources. "Canadian Journal of Forest Research" 41: 59–72.
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