Estimating Tree Height-Diameter Models with the Bayesian Method
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 met...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/683691 |
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| author | Xiongqing Zhang Aiguo Duan Jianguo Zhang Congwei Xiang |
| author_facet | Xiongqing Zhang Aiguo Duan Jianguo Zhang Congwei Xiang |
| author_sort | Xiongqing Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-68fd9eac72dc4751807ab9f0e73b74a4 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-68fd9eac72dc4751807ab9f0e73b74a42025-08-20T02:35:25ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/683691683691Estimating Tree Height-Diameter Models with the Bayesian MethodXiongqing Zhang0Aiguo Duan1Jianguo Zhang2Congwei Xiang3State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, ChinaState Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, ChinaState Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, ChinaState Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, ChinaSix 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.http://dx.doi.org/10.1155/2014/683691 |
| spellingShingle | Xiongqing Zhang Aiguo Duan Jianguo Zhang Congwei Xiang Estimating Tree Height-Diameter Models with the Bayesian Method The Scientific World Journal |
| title | Estimating Tree Height-Diameter Models with the Bayesian Method |
| title_full | Estimating Tree Height-Diameter Models with the Bayesian Method |
| title_fullStr | Estimating Tree Height-Diameter Models with the Bayesian Method |
| title_full_unstemmed | Estimating Tree Height-Diameter Models with the Bayesian Method |
| title_short | Estimating Tree Height-Diameter Models with the Bayesian Method |
| title_sort | estimating tree height diameter models with the bayesian method |
| url | http://dx.doi.org/10.1155/2014/683691 |
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