Prediction of tree diameter growth using quantile regression and mixed-effects models

By Som Bohora, Q. V. Cao. 2014

53 citations recorded by the source corpus.

Subfields: Building and Construction, Mechanical Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f7e6a011fb4124166464af926ca6260630abf3fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Q. V. Cao
2. Xiangdong Lei
3. Hao Zang
4. Weisheng Zeng
5. Guillermo Trincado
6. Ramazan Özçelik
7. Nilsun Göçer

In Mechanical Engineering

1. Xiangdong Lei
2. Hao Zang
3. Weisheng Zeng
4. Guillermo Trincado
5. Nilsun Göçer
6. Q. V. Cao
7. Ramazan Özçelik

In Nature and Landscape Conservation

1. Xiangdong Lei
2. Hao Zang
3. Weisheng Zeng
4. Q. V. Cao
5. Guillermo Trincado
6. Nilsun Göçer
7. Ramazan Özçelik

Citations in the corpus

• Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey (2018, methodology): “We used the two-fold evaluation scheme (Bohora and Cao, 2014), in which parameters of the h-d model fitted to one group was applied to predict for the other group.”
• Height–diameter equations for larch plantations in northern and northeastern China: a comparison of the mixed-effects, quantile regression and generalized additive models (2016, methodology): “Recently QR has been applied in forest growth models, for example, self-thinning boundary lines (Zhang et al., 2005), tree diameter percentiles (Mehtätalo et al., 2008; Bohora and Cao, 2014), stand density index (Ducey and Knapp, 2010) and height–diameter model (Rust, 2014).”