文献类型：期刊文献

英文题名：Estimating Tree Height-Diameter Models with the Bayesian Method

作者：Zhang, Xiongqing[1] Duan, Aiguo[1] Zhang, Jianguo[1] Xiang, Congwei[1]

第一作者：张雄清

通信作者：Zhang, JG[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, Key Lab Tree Breeding & Cultivat State Forestry A, State Key Lab Tree Genet & Breeding, Beijing 100091, Peoples R China

年份：2014

卷号：2014

外文期刊名：SCIENTIFIC WORLD JOURNAL

收录：MEDLINE(收录号：24711733);;Scopus(收录号：2-s2.0-84898795704);WOS:【SCI-EXPANDED(收录号:WOS:000332643200001)】；PubMed;

基金：The authors are grateful to the editor and two anonymous reviewers for their valuable suggestions and comments on the paper. Funding for the study was provided by National Natural Science Foundation of China (no. 31300537 and no. 31370629), the Research Institute of Forestry, Chinese Academy of Forestry, for fund support for young scholars (no. RIF2013-09), and collaborative innovation plan of Jiangsu Higher Education.

语种：英文

摘要：Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the "best" model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.