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Category Text Publication
Reference Category Journals
DOI 10.1007/s00477-007-0117-2
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Title (Primary) Analyzing spatial ecological data using linear regression and wavelet analysis
Author Carl, G.; Kühn, I. ORCID logo
Journal Stochastic Environmental Research and Risk Assessment
Year 2008
Department BZF
Volume 22
Issue 3
Page From 315
Page To 324
Language englisch
Keywords Binary data; Distribution data; Multiple regression; Normal data; Spatial autocorrelation
Abstract Spatial (two-dimensional) distributions in ecology are often influenced by spatial autocorrelation. In standard regression models, however, observations are assumed to be statistically independent. In this paper we present an alternative to other methods that allow for autocorrelation. We show that the theory of wavelets provides an efficient method to remove autocorrelations in regression models using data sampled on a regular grid. Wavelets are particularly suitable for data analysis without any prior knowledge of the underlying correlation structure. We illustrate our new method, called wavelet-revised model, by applying it to multiple regression for both normal linear models and logistic regression. Results are presented for computationally simulated data and real ecological data (distribution of species richness and distribution of the plant species Dianthus carthusianorum throughout Germany). These results are compared to those of generalized linear models and models based on generalized estimating equations. We recommend wavelet-revised models, in particular, as a method for logistic regression using large datasets.
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Carl, G., Kühn, I. (2008):
Analyzing spatial ecological data using linear regression and wavelet analysis
Stoch. Environ. Res. Risk Assess. 22 (3), 315 - 324