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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
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Related Experiment Video

Updated: Dec 12, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

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Accurate likelihood inference for the volume under the ROC surface.

Erlis Ruli1, Laura Ventura1

  • 1Department of Statistical Sciences, University of Padua, Padua, Italy.

Cancer Reports (Hoboken, N.J.)
|August 15, 2020
PubMed
Summary

Higher-order likelihood inference improves diagnostic accuracy estimation for continuous tests, especially in small samples. The R package likelihoodAsy simplifies applying these advanced methods for accurate results.

Keywords:
AUCVUSdiagnostic accuracyhigher order likelihood inferencesmall sample sizestress-strength model

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Inference

Background:

  • The volume under the ROC surface is a key metric for diagnostic accuracy with ordered categories.
  • Classical inference for ROC curves can be unreliable with small sample sizes.

Purpose of the Study:

  • To demonstrate higher-order likelihood procedures for accurate parametric inference in small samples.
  • To provide precise point estimates and confidence intervals for ROC surface volume.

Main Methods:

  • Utilized higher-order asymptotic methods for statistical inference.
  • Conducted simulation studies to validate the proposed methodology.
  • Applied the methods to two real-world datasets.

Main Results:

  • Modern likelihood inference refines classical results for diagnostic accuracy.
  • The R package likelihoodAsy facilitates straightforward implementation of these methods.

Conclusions:

  • Higher-order likelihood inference offers improved accuracy for ROC surface volume estimation.
  • The likelihoodAsy package overcomes implementation challenges, making advanced methods accessible.