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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TabularUnified References-Bibliography
v30 v31 32 32 [[BR]]http://www.hlug.de/twinning/water/dokumente/rubrik_documents/GISCO_CCM_evaluation.pdf 33 33 34 35 34 http://www.ec-gis.org/Workshops/8ec-gis/cd/papers/3_crd_jv.pdf 36 35 [[BR]]http://www.ec-gis.org/Workshops/8ec-gis/presentations/pdf/3_crd_jv.pdf … … 58 57 http://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 59 58 59 Larinier M. (2000). Dams and Fish Migration. World Commission on Dams, Toulouse, France. 60 http://www.dams.org/damsreport-site/docs/kbase/contrib/env247.pdf 61 60 62 = Corine Land Cover CLC = 61 63 … … 79 81 80 82 = 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, 160-169.82 83 83 84 AUSTIN 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, 1149-1163.98 85 99 86 Barry, S. C., andWelsh, A. H. (2002), “Generalized Additive Modelling and Zero Inflated Count Data,” !EcologicalModelling, 157, 179–188. … … 103 90 Fletcher, 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. 104 91 92 HORTON N. J. and LIPSITZ S. R., 1999. Review of software to fit generalized estimating equation regression models. American Statistician, 53, 2, 160-169. 93 105 94 Johnson, N.L., Kemp, A.W., and Kotz, S. (2005), Univariate Discrete Distributions (3rd ed.), New York: Wiley. 106 95 107 96 Lambert, D. (1992), “Zero-Inflated Poisson Regression,With an Application to Defects in Manufacturing,” Technometrics,34, 1–14. 108 97 98 LIANG K. Y. and ZEGER S. L., 1986. Longitudinal data analysis using generalized linear models. Biometrika, 73, 1, 13-22. 99 100 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. 101 109 102 Martin,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. 110 103 104 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. 105 106 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. 107 108 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. 109 110 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. 111 111 112 Welsh, 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. 113 114 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. 112 115 113 116 == Bootstrap ==