Distance-based parametric bootstrap tests for clustering of species ranges

DOI10.1016/S0167-9473(03)00091-4zbMath1429.62068OpenAlexW2067144619WikidataQ60471659 ScholiaQ60471659MaRDI QIDQ956894

Christian Hennig, Bernhard Hausdorf

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(03)00091-4

zbMATH Keywords

Monte Carlospatial autocorrelationpresence-absence datadouble bootstrapbiogeographyclustering under noise

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Parametric hypothesis testing (62F03) Bootstrap, jackknife and other resampling methods (62F40)

Related Items

Testing independence between two nonhomogeneous point processes in time, Bayesian and Classical Approaches for Hypotheses Testing in Trisomies, CART algorithm for spatial data: application to environmental and ecological data, Type I errors linked to faulty statistical analyses of endangered subspecies classifications, Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters, Cluster-wise assessment of cluster stability

Uses Software

• spatial
• clusfind
• bootlib

Cites Work

• Validating visual clusters in large datasets: fixed point clusters of spectral features.
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• Prepivoting Test Statistics: A Bootstrap View of Asymptotic Refinements
• Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• A Probability Theory of Cluster Analysis
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