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广义Logistic回归模型Bayes分析及其在林木存活率预报中的应用(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2010年02期
Page:
47-50
Column:
研究论文
publishdate:
2010-03-30

Article Info:/Info

Title:
Bayes analysis for generalized Logistic regression model and its application to forestry survival rate
Author(s):
XIA Yemao LIU YinganFANG Zheng
College of Science, Nanjing Forestry University, Nanjing 210037, China
Keywords:
generalized Logistic regression model Markov Chains Monte Carlo Metroplis Hastings algorithm forestry survival rate
Classification number :
O212; S764
DOI:
10.3969/j.jssn.1000-2006.2010.02.010
Document Code:
A
Abstract:
Factor analysis, which is characterized by the latent variables, is a popular method to interpret the correlation among the observed variables. In this paper, latent constructs are introduced to describe the relationship of the categorical variables. Within the Bayesian framework, parameters estimations and statistical inferences are carried out via a popular technique, i.e., Markov Chains Monte Carlo (MCMC). A simulation study is conducted to assess the proposed method. A pika data set is used to illustrate the real application.

References

[1]Agresti A. Categorical Data Analysis[M]. NewYork: John Wiley and Sons Inc, 2002.
[2]We B C. Exponential Family Nonlinear Models[M]. Singapore: Springer, 1998.
[3]熊林平,曹秀堂,徐勇勇,等. 纵向观测二分类数据的广义线性模型分析[J]. 第二军医大学学 报,1999,20(7):483-485.
[4]曹铭昌,周广胜,翁恩生. 广义模型及分类回归树在物种分布模拟中的应用与比较[J]. 生态学 报,2005,25(8):2031-2040.
[5]Choi T, Schervish M J, Schmitt K A, et al. A Bayesian approach to Logistic regression model with incomplete information[J]. Biometrics, 2008, 64(2): 424-431.
[6]Bollen K A. Structural equations with latent variable[M]. New York: Wiley, 1989.
[7]Berger J O. Statistical Decision Theory and Bayesian Analysis[M]. New York: Springer Verlag, 1980.
[8]Gilks W R, Richardson S, Spiegelhalter D J. Makov Chain Monte Carlo in Practice [C]//London, England: Chapman and Hall, 1996.
[9]Tanner M A, Wong W H. The calculation of posterior distributions by data augmentation (with discussion)[J]. Journal of the American Statistical Association, 1987, 8: 528-550.
[10]Geman S, Geman D. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984.
[11]Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equations of state calculations bu fast computing machines[J]. Journal of Chemical Physics, 1953, 21: 1807-1092.
[12]Hastings W K. Monte Carlo sampling methods using Markov chains and their applications[J]. Biometrika, 1970, 57: 97-100.
[13]Tierney L. Markov Chains for exploring posterior distributions (with discussion)[J]. The Annals of Statistics, 1994, 22: 1701-1786.
[14]郭爱花,陈钰,符利勇. 野兔对人工幼林危害的研究[J]. 山东林业科技,2009,39(1):54-55.
[15]Kass R E, Raftery A E. Bayes factors[J]. Journal of the American Statistical Association, 1995, 90: 773-795.
[16]Gelman A, Meng X L. Simulating normalizing constants: From importance sampling to bridge sampling to pathing sampling[J]. Statistical Sciences, 1998, 13, 163-185.
[17]Lee S Y. Structural Equation Modeling: A Bayesian Perspective[M]. Singapore: John Wiley, 2007.

Last Update: 2010-05-14