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    Author(s): G. L. Gadbury; H. K. lyer; H. T. Schreuder; C. Y. Ueng
    Date: 1997
    Source: Res. Pap. RMRS-RP-2. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 14 p.
    Publication Series: Research Paper (RP)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (1.17 MB)

    Description

    Tree based statistical models can be used to investigate data structure and predict future observations. We used nonparametric and nonlinear models to reexamine the data sets on tree growth used by Bechtold et al. (1991) and Ruark et al. (1991). The growth data were collected by Forest Inventory and Analysis (FIA) teams from 1962 to 1972 (4th cycle) and 1972 to 1982 (5th cycle). We used tree based models to group observations into clusters that were specified by covariate values. Next, we performed a permutation test on the grouped data to test for a change in tree growth rates from the 4th cycle to the 5th cycle. Our techniques differed from those used by Bechtold et al. (1991) and Ruark et al. (1991). The data was not assumed to follow any parametric distribution, the relation between response and covariates was not assumed to be linear, and the test for a change in tree growth did not require any parametric assumptions. The methodology presented here is general and applicable to other situations where the significance of a specific covariate is in question. Despite these relaxed constraints of analysis, our results generally agreed with those of Bechtold et al. and Ruark et al.

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    Citation

    Gadbury, G. L.; lyer, H. K.; Schreuder, H. T.; Ueng, C. Y. 1997. A nonparametric analysis of plot basal area growth using tree based models. Res. Pap. RMRS-RP-2. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 14 p.

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    Keywords

    nonparametric, nonlinear, models, tree based statistical models, tree growth rates, parametric distribution, tree growth data

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