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Generalized linear and generalized additive models in studies of species distributions: setting the scene

机译:物种分布研究中的广义线性模型和广义加性模型:场景设置

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An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. (C) 2002 Elsevier Science B.V. All rights reserved. [References: 81]
机译:过去30年的重要统计发展是广义线性模型(GLM)和广义加性模型(GAM)提供的回归分析的进步。在这里,我们介绍一系列在国际研讨会的框架内准备的一系列论文,该研讨会的主题是:GLM / GAM建模的进展:从物种分布到环境管理,于2001年8月6日至11日在瑞士的里德阿尔普举行。生态统计模型,并简要回顾了在生态建​​模工作中使用GLM和GAM的几个关键示例。接下来,我们概述GLM和GAM,并讨论用于预测变量选择,模型诊断和评估的一些相关统计数据。其中包括对适用于GLM和GAM的几种新方法的讨论,例如岭回归,逐步选择预测变量的替代方法,以及通过结合使用回归树和其他几种方法来识别相互作用的方法。最后,我们对论文进行了概述,并认为它们对我们将其应用于生态模型的理解有所帮助。 (C)2002 Elsevier Science B.V.保留所有权利。 [参考:81]

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