Spatial prediction in the presence of left-censoring
2014 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, Vol. 74, 125-141 p.Article in journal (Other academic) Published
Environmental (spatial) monitoring of different variables often involves left-censored observations falling below the minimum detection limit (MDL) of the instruments used to quantify them. Several methods to predict the variables at new locations given left-censored observations of a stationary spatial process are compared. The methods use versions of kriging predictors, being the best linear unbiased predictors minimizing the mean squared prediction errors. A semi-naive method that determines imputed values at censored locations in an iterative algorithm together with variogram estimation is proposed. It is compared with a computationally intensive method relying on Gaussian assumptions, as well as with two distribution-free methods that impute the MDL or MDL divided by two at the locations with censored values. Their predictive performance is compared in a simulation study for both Gaussian and non-Gaussian processes and discussed in relation to the complexity of the methods from a user’s perspective. The method relying on Gaussian assumptions performs, as expected, best not only for Gaussian processes, but also for other processes with symmetric marginal distributions. Some of the (semi-)naive methods also work well for these cases. For processes with skewed marginal distributions (semi-)naive methods work better. The main differences in predictive performance arise for small true values. For large true values no difference between methods is apparent.
Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 74, 125-141 p.
kriging, left-censoring, minimum detection limit, prediction, spatial process
Probability Theory and Statistics
Research subject Mathematical Statistics
IdentifiersURN: urn:nbn:se:umu:diva-53278DOI: 10.1016/j.csda.2014.01.004ISI: 000333781500010OAI: oai:DiVA.org:umu-53278DiVA: diva2:511108
Originally published in dissertation in manuscript form.2012-03-202012-03-192014-05-05Bibliographically approved