Title :
Random Coefficient Model of Basal Area Growth for Longitudinal Data
Author :
Qing, Zhang ; Junhui, Zhao ; Xingang, Kang
Author_Institution :
Coll. of Sci., Beijing Forestry Univ., Beijing, China
Abstract :
Basal Area Growth model play an important role in forest management. In the permanent forest plots, the measures of BA growth are taken repeatedly over time, each stand has its individual trajectory. It is necessary to model both main response and individual trajectory of forest stands BA. In this paper, we show how a new random coefficient model of stands Basal Area Growth, which is developed based on longitudinal data. Through comparing the goodness of fit Statistics for different error structures, the optimal model is with AR(1) error structure.
Keywords :
forestry; statistical analysis; basal area growth model; forest management; longitudinal data; random coefficient model; Computational modeling; Educational institutions; Error analysis; Forestry; Laboratories; Sea measurements; Soil; Synthetic aperture sonar; Time measurement; Vegetation;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.636
