Space–time regression modeling of tree growth using the skew-t distribution

Authors

  • F. S. Nathoo

    Corresponding author
    1. Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada V8W 3R4
    • Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada V8W 3R4.
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  • This article is published in Environmetrics as a special issue on TIES 2008: Quantitative Methods for Environmental Sustainability, edited by Sylvia R. Esterby, University of British Columbia Okanagan, Canada.

Abstract

In this paper, we present new statistical methodology for the analysis of repeated measures of spatially correlated growth data. Our motivating application, a 10 year study of height growth in a plantation of even-aged white spruce, presents several challenges for statistical analysis. Here, the growth measurements arise from an asymmetric distribution, with heavy tails, and thus standard longitudinal regression models based on a Gaussian error structure are not appropriate. We seek more flexibility for modeling both skewness and fat tails, and achieve this within the class of skew-elliptical distributions. Within this framework, robust space–time regression models are formulated using random effect growth curves, with coefficients arising from an underlying multivariate spatial process. Computational difficulties arise when data are collected at a large number of locations, and we consider two approaches for spatial modeling in the large data context. Both approaches are compared within the context of our application, and inference is conducted in a Bayesian framework, with implementation based on hybrid Monte Carlo. Copyright © 2010 John Wiley & Sons, Ltd.

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