Changes between Version 30 and Version 31 of References-Bibliography


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Jul 22, 2010 11:22:16 AM (15 years ago)
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celine
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  • TabularUnified References-Bibliography

    v30 v31  
    3232[[BR]]http://www.hlug.de/twinning/water/dokumente/rubrik_documents/GISCO_CCM_evaluation.pdf 
    3333 
    34  
    3534http://www.ec-gis.org/Workshops/8ec-gis/cd/papers/3_crd_jv.pdf 
    3635[[BR]]http://www.ec-gis.org/Workshops/8ec-gis/presentations/pdf/3_crd_jv.pdf 
     
    5857http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VBS-4X26XPY-3&_user=5403746&_coverDate=10%2F24%2F2009&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1408266810&_rerunOrigin=google&_acct=C000037979&_version=1&_urlVersion=0&_userid=5403746&md5=4ffe13f701ad67910e412fe2858c20b8 
    5958 
     59Larinier M. (2000). Dams and Fish Migration. World Commission on Dams, Toulouse, France. 
     60http://www.dams.org/damsreport-site/docs/kbase/contrib/env247.pdf 
     61 
    6062= Corine Land Cover CLC = 
    6163 
     
    7981 
    8082= Analyses statistiques = 
    81 HORTON N. J. and LIPSITZ S. R., 1999. Review of software to fit generalized estimating equation regression models. American Statistician, 53, 2, 160-169. 
    8283 
    8384AUSTIN M. P., 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 2-3, 101-118. 
    84  
    85 MILLER J. and FRANKLIN J., 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modelling, 157, 2-3, 227-247. 
    86  
    87 PYPER B. J. and PETERMAN R. M., 1998. Comparison of methods to account for autocorrelation in correlation analyses of fish data. Canadian Journal of Fisheries and Aquatic Sciences, 55, 9, 2127-2140. 
    88  
    89 NISHIDA T. and CHEN D.-G., 2004. Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna (Thunnus albacares) longline CPUE data. Fisheries Research, 70, 2-3, 265-274. 
    90  
    91 OLIVIER F. and WOTHERSPOON S. J., In Press, Corrected Proof. Modelling habitat selection using presence-only data: Case study of a colonial hollow nesting bird, the snow petrel. Ecological Modelling. 
    92  
    93 WU W., HALL C. and ZHANG L., 2006. Predicting the temporal and spatial probability of orographic cloud cover in the Luquillo Experimental Forest in Puerto Rico using generalized linear (mixed) models. Ecological Modelling, 192, 3-4, 473-498. 
    94  
    95 LIANG K. Y. and ZEGER S. L., 1986. Longitudinal data analysis using generalized linear models. Biometrika, 73, 1, 13-22. 
    96  
    97 LIPSITZ S. R., KIM K. and ZHAO L. P., 1994. Analysis of Repeated Categorical-Data Using Generalized Estimating Equations. Statistics in Medicine, 13, 11, 1149-1163. 
    9885 
    9986Barry, S. C., andWelsh, A. H. (2002), “Generalized Additive Modelling and Zero Inflated Count Data,” !EcologicalModelling, 157, 179–188. 
     
    10390Fletcher, D. J., !MacKenzie, D. I., and Villouta, E. (2005), “Modelling Skewed Data with Many Zeros: A Simple Approach Combining Ordinary and Logistic Regression,” Environmental and Ecological Statistics, 12, 45–54. 
    10491 
     92HORTON N. J. and LIPSITZ S. R., 1999. Review of software to fit generalized estimating equation regression models. American Statistician, 53, 2, 160-169. 
     93 
    10594Johnson, N.L., Kemp, A.W., and Kotz, S. (2005), Univariate Discrete Distributions (3rd ed.), New York: Wiley. 
    10695 
    10796Lambert, D. (1992), “Zero-Inflated Poisson Regression,With an Application to Defects in Manufacturing,” Technometrics,34, 1–14. 
    10897 
     98LIANG K. Y. and ZEGER S. L., 1986. Longitudinal data analysis using generalized linear models. Biometrika, 73, 1, 13-22. 
     99 
     100LIPSITZ S. R., KIM K. and ZHAO L. P., 1994. Analysis of Repeated Categorical-Data Using Generalized Estimating Equations. Statistics in Medicine, 13, 11, 1149-1163. 
     101 
    109102Martin,T.G.,Wintle,B.A., Rhodes, J. R.,Kuhnert, P.M., Field, S. A.,Low-Choy, S. J.,Tyre, A. J., and Possingham,H. P. (2005), “Zero Tolerance Ecology: Improving Ecological Inference by Modelling the Source of Zero Observations,” Ecology Letters, 8, 1235–1246. 
    110103 
     104MILLER J. and FRANKLIN J., 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modelling, 157, 2-3, 227-247. 
     105 
     106NISHIDA T. and CHEN D.-G., 2004. Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna (Thunnus albacares) longline CPUE data. Fisheries Research, 70, 2-3, 265-274. 
     107 
     108OLIVIER F. and WOTHERSPOON S. J., In Press, Corrected Proof. Modelling habitat selection using presence-only data: Case study of a colonial hollow nesting bird, the snow petrel. Ecological Modelling. 
     109 
     110PYPER B. J. and PETERMAN R. M., 1998. Comparison of methods to account for autocorrelation in correlation analyses of fish data. Canadian Journal of Fisheries and Aquatic Sciences, 55, 9, 2127-2140. 
     111 
    111112Welsh, A. H., Cunningham, R. B., Donnelly, C. F., and Lindenmayer, D. B. (1996), “Modelling the Abundance of Rare Species: Statistical Models for Counts With Extra Zeros,” Ecological Modelling, 88, 297–308. 
     113 
     114WU W., HALL C. and ZHANG L., 2006. Predicting the temporal and spatial probability of orographic cloud cover in the Luquillo Experimental Forest in Puerto Rico using generalized linear (mixed) models. Ecological Modelling, 192, 3-4, 473-498. 
    112115 
    113116== Bootstrap ==