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Activity Number: 190 - Contributed Poster Presentations: Section on Statistics and the Environment
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #305198
Title: Mixed-Effect Model Using Shape-Constrained Regression Splines, with Application to Tree Height Estimation
Author(s): Xiyue Liao* and Mary C Meyer
Companies: University of California, Santa Barbara and Colorado State University
Keywords: mixed-effect; spline estimator ; shape constraint ; confidence interval ; hypothesis test ; convergence rate

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.

Authors who are presenting talks have a * after their name.

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