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Kamo, Ken-Ichi,Yanagihara, Hirokazu,Kato, Akio,Yoshimoto, Atsushi Institute of Forest Science 2008 Journal of Forest Science Vol.24 No.3
In this paper, we apply a logistic regression model to the data of snow damage on sugi (Cryptomeria japonica) occurred in Toyama prefecture (in Japan) in 2004 for estimating the risk probability. In order to specify the factors effecting snow damage, we apply a model selection procedure determining optimal subset of explanatory variables. In this process we consider the following 3 information criteria, 1) Akaike's information criterion, 2) Baysian information criterion, 3) Bias-corrected Akaike's information criterion. For the selected variables, we give a proper interpretation from the viewpoint of natural disaster.
Ken-ichi Kamo,Atsushi Yoshimoto 한국산림과학회 2013 Forest Science And Technology Vol.9 No.2
Growth models are in general constructed by some kind of mathematical function, so-called “growth function”. Since there are several possible growth functions, the first task for the growth analysis is to select the most suitable growth function for the target data among candidates. In this paper, three statistical procedures are presented and compared on the basis of the information criterion for the purpose of selecting a growth function. A demonstrative example is provided to show how the three statistical procedures differ and select different growth functions for the same growth data, and how differently the amount of carbon sequestrated in a forest stand is affected by the selected growth function.
Ken-ichi Kamo,Hirokazu Yanagihara,Akio Kato,Atsushi Yoshimoto 강원대학교 산림과학연구소 2008 Journal of Forest Science Vol.24 No.3
In this paper, we apply a logistic regression model to the data of snow damage on sugi (Cryptomeria japonica) occurred in Toyama prefecture (in Japan) in 2004 for estimating the risk probability. In order to specify the factors effecting snow damage, we apply a model selection procedure determining optimal subset of explanatory variables. In this process we consider the following 3 information criteria, 1) Akaike’'s information criterion, 2) Baysian information criterion, 3) Bias-corrected Akaike’'s information criterion. For the selected variables, we give a proper interpretation from the viewpoint of natural disaster.