Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31044
Authors: 
Kneib, Thomas
Müller, Jörg
Hothorn, Torsten
Year of Publication: 
2006
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 492
Abstract: 
Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of the observations is often violated in practice since the points at which the observations are collected are spatially aligned. While several approaches have been developed to analyse and account for spatial correlation in regression models with normally distributed responses, far less work has been done in the context of generalised linear models. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations.
Subjects: 
bivariate penalised splines
generalised linear models
geostatistics
kriging
spatial autocorrelatio
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
685.83 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.