New applications of statistical tools in plant pathology

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dc.contributor.author Madden, L. V.
dc.contributor.author Hughes, G.
dc.contributor.author Pfender, W. F.
dc.contributor.author Garrett, Karen A.
dc.date.accessioned 2012-06-04T18:34:52Z
dc.date.available 2012-06-04T18:34:52Z
dc.date.issued 2012-06-04
dc.identifier.uri http://hdl.handle.net/2097/13902
dc.description.abstract The series of papers introduced by this one address a range of statistical applications in plant pathology, including survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. Here we present an overview of additional applications of statistics in plant pathology. An analysis of variance based on the assumption of normally distributed responses with equal variances has been a standard approach in biology for decades. Advances in statistical theory and computation now make it convenient to appropriately deal with discrete responses using generalized linear models, with adjustments for overdispersion as needed. New nonparametric approaches are available for analysis of ordinal data such as disease ratings. Many experiments require the use of models with fixed and random effects for data analysis. New or expanded computing packages, such as SAS PROC MIXED, coupled with extensive advances in statistical theory, allow for appropriate analyses of normally distributed data using linear mixed models, and discrete data with generalized linear mixed models. Decision theory offers a framework in plant pathology for contexts such as the decision about whether to apply or withhold a treatment. Model selection can be performed using Akaike's information criterion. Plant pathologists studying pathogens at the population level have traditionally been the main consumers of statistical approaches in plant pathology, but new technologies such as microarrays supply estimates of gene expression for thousands of genes simultaneously and present challenges for statistical analysis. Applications to the study of the landscape of the field and of the genome share the risk of pseudoreplication, the problem of determining the appropriate scale of the experimental unit and of obtaining sufficient replication at that scale. en_US
dc.relation.uri http://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO.2004.94.9.999 en_US
dc.subject Statistical tools en_US
dc.subject Plant pathology en_US
dc.title New applications of statistical tools in plant pathology en_US
dc.type Article (publisher version) en_US
dc.date.published 2004 en_US
dc.citation.doi doi:10.1094/PHYTO.2004.94.9.999 en_US
dc.citation.epage 1003 en_US
dc.citation.issue 9 en_US
dc.citation.jtitle Phytopathology en_US
dc.citation.spage 999 en_US
dc.citation.volume 94 en_US
dc.contributor.authoreid kgarrett en_US

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