Gudrun Carl

TL;DR: It was found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection and the value of GLM in combination with penalised methods and thresholds when omitted variables are considered in the final interpretation.

TL;DR: In this paper, the authors describe six different statistical approaches to infer correlates of species distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations.

TL;DR: In this article, the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR err, lagged = SAR lag and mixed = SAR mix ) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial auto-correlation structures.

TL;DR: In this paper, a generalized estimating equation (GEE) was used to analyze the influence of autocorrelation of observations on logistic regression models for two-dimensional macroecological and biogeographical data sets displaying spatial autocorecorrelation.

TL;DR: In this article, a wavelet-revised model is proposed to remove autocorrelations in regression models using data sampled on a regular grid, which is suitable for data analysis without any prior knowledge of the underlying correlation structure.