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Comparing strategies for modeling tree diameter percentiles from remeasured plots
Author(s) -
Mehtätalo Lauri,
Gregoire Timothy G.,
Burkhart Harold E.
Publication year - 2008
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.896
Subject(s) - percentile , statistics , quantile , mathematics , linear regression , population , sample size determination , conditional probability distribution , quantile regression , sample (material) , sampling (signal processing) , regression , tree (set theory) , econometrics , computer science , filter (signal processing) , mathematical analysis , chemistry , demography , chromatography , sociology , computer vision
Abstract In many situations, including forest management planning, the underlying diameter distribution of a forest stand needs to be predicted. One alternative for predicting diameter distribution involves modeling diameter percentiles. We introduce a quantile regression (QR) approach for predicting diameter percentiles, and compare it with customary linear fixed‐effect and linear mixed‐effects models. The customary methods involve first estimating plot‐specific sample percentiles and then regressing them on stand characteristics, whereas QR directly models percentiles. We compared two prediction situations: a conditional one where a previously measured diameter sample is available from the stand of interest, and a marginal one where only some stand‐specific characteristics are known. To compare the predictions to the true underlying percentiles, we conducted a simulation study. The QR approach led to slight improvements in the marginal prediction situation. In the conditional situation, the mixed‐effect model led to clearly better predictions and should be preferred until QR methods have been developed for hierarchical data. It is extremely important to filter out the highly correlated sampling error from conditional predictions in order that the models predict underlying population percentiles rather than the realized sample percentiles. Copyright © 2007 John Wiley & Sons, Ltd.