This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the clas...This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.展开更多
We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measu...We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measures such as sensitivity,specificity and area under the ROC curve are no longer applicable.In recent literature,new diagnostic accuracy measures are introduced in medical research studies.In this paper,important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples.We offer problem-based R code to illustrate how to perform these statistical computations step by step.We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.Our program can be adapted to many classifiers among which logistic regression may be the most popular approach.We thus base our discussion and illustration completely on the logistic regression in this paper.展开更多
文摘This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.
基金Li’s work was partially supported by National Medical Research Council in Singapore and AcRF R-155-000-174-114.NNSF[grant number 11371142].
文摘We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measures such as sensitivity,specificity and area under the ROC curve are no longer applicable.In recent literature,new diagnostic accuracy measures are introduced in medical research studies.In this paper,important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples.We offer problem-based R code to illustrate how to perform these statistical computations step by step.We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.Our program can be adapted to many classifiers among which logistic regression may be the most popular approach.We thus base our discussion and illustration completely on the logistic regression in this paper.
