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Activity Number:
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190
- Contributed Poster Presentations: Section on Statistics and the Environment
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #305198
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Title:
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Mixed-Effect Model Using Shape-Constrained Regression Splines, with Application to Tree Height Estimation
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Author(s):
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Xiyue Liao* and Mary C Meyer
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Companies:
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University of California, Santa Barbara and Colorado State University
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Keywords:
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mixed-effect;
spline estimator ;
shape constraint ;
confidence interval ;
hypothesis test ;
convergence rate
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Abstract:
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Estimation of tree height given diameter is an important part of the Forest Inventory Analysis of the US Forest Service. Existing methods use parametric models to estimate the curve. We propose a semi-parametric model in which log(height) is a smooth, increasing and concave function of diameter, with random plot component and fixed-effect covariates. Proposed inference methods include point-wise confidence intervals for the smooth fixed effect, prediction intervals for new observations, and a likelihood ratio test for the significance of the random effect. The methods are implemented by the cgamm routine in the R package cgam and can be used for a wide range of mixed-model applications.
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Authors who are presenting talks have a * after their name.
