The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Permutational Multivariate Analysis of Variance ( PERMANOVA )
Abstract
Permutational multivariate analysis of variance (PERMANOVA) is a geometric partitioning of variation across a multivariate data cloud, defined explicitly in the space of a chosen dissimilarity measure, in response to one or more factors in an analysis of variance design. Statistical inferences are made in a distribution‐free setting using permutational algorithms. The PERMANOVA framework is readily extended to accommodate random effects, hierarchical models, mixed models, quantitative covariates, repeated measures, unbalanced and/or asymmetrical designs, and, most recently, heterogeneous dispersions among groups. Plots to accompany PERMANOVA models include ordinations of either fitted or residualized distance matrices, including multivariate analogues to main effects and interaction plots, to visualize results.
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Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828
Self-citationBootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350
Distance‐Based Tests for Homogeneity of Multivariate Dispersions
Anderson, M. J. (2006) · Biometrics · doi:10.1111/j.1541-0420.2005.00440.x · Cited by 3,304
Self-citationEdgington, E., Edgington, E., & Onghena, P. (2007) · doi:10.1201/9781420011814 · Cited by 2,030
Searle, S. R., Casella, G., & McCulloch, C. E. (1992) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316856 · Cited by 1,926
Average Values of Mean Squares in Factorials
Cornfield, J., & Tukey, J. W. (1956) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177728067 · Cited by 292
Cited by
Li, L., Ning, Z., Zhang, X., Mayne, J., Cheng, K., Stintzi, A., & Figeys, D. (2020) · Microbiome · doi:10.1186/s40168-020-00806-z · Cited by 328
Zhang, X., Zhong, H., Li, Y., Shi, Z., Ren, H., Zhang, Z., Zhou, X., Tang, S., Han, X., Lin, Y., et al. (2021) · Nature Aging · doi:10.1038/s43587-020-00014-2 · Cited by 250
Samstein, R. M., Krishna, C., Ma, X., Pei, X., Lee, K.-W., Makarov, V., Kuo, F., Chung, J., Srivastava, R. M., Purohit, T. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-00139-8 · Cited by 219
Taylor, B. C., Lejzerowicz, F., Poirel, M., Shaffer, J. P., Jiang, L., Aksenov, A., Litwin, N., Humphrey, G., Martino, C., Miller-Montgomery, S., et al. (2020) · mSystems · doi:10.1128/msystems.00901-19 · Cited by 203
Corinaldesi, C., Marcellini, F., Nepote, E., Damiani, E., & Danovaro, R. (2018) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.05.108 · Cited by 163
Metadata
- Authors
- Anderson, M. J.
- Venue
- Wiley StatsRef: Statistics Reference Online
- Publisher
- Wiley
- Published
- 2017-11-15
- Pages
- 1-15
- OpenAlex
- W2802817419
- Cited by
- 1,849
- References
- 7
- Type
- other
- Language
- en
- Open access
- closed
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