nonbinROC: Software for Evaluating Diagnostic Accuracies with Non-Binary Gold Standards

ROC analysis is a standard method for estimating and comparing diagnostic tests' accuracies when the gold standard is binary. However, there are many situations when the gold standard is not binary. In these situations, traditional ROC methods applied have lead to biased and uninformative outcomes. This article introduces nonbinROC, software for R that implements nonparametric estimators proposed by Obuchowski (2005) for estimating and comparing diagnostic tests' accuracies when the gold standard is measured on a continuous, ordinal or nominal scale. The results produced from these estimators are interpreted in the same manner as in ROC analysis but are not associated with any ROC curve.

Submission of Evidence on Online Violence Against Women to the UN Special Rapporteur on Violence Against Women, its Causes and Consequences, Dr Dubravka Šimonović

Figure S1. B3GALNT2 levels determined by W.B. and ROC curve. a–c Relative mRNA expression of B3GALNT2 in HCC tumor tissues and normal liver tissues obtained from GSE76427, GSE36376, and TCGA-LIHC datasets. d Western blot analysis of B3GALNT2 levels in 24 pairs of HCC tissues. T HCC tumor tissue, N adjacent non-tumor tissue. e ROC curve analysis of the sensitivity and specificity for the predictive value of TNM model, B3GALNT2 expression, and the combination model. (TIFF 546 kb

Macedonian-Romanian Church Relations

The subject of this paper is a content analysis of the articles in the media about the Macedonian-Romanian Orthodox Church relations. According to the content analysis of the sample of articles in the media about the relations between the Macedonian Orthodox Church - the Ohrid Archbishopric (MOC-OA) and the Romanian Orthodox Church (ROC), it can be concluded that the analyzed texts are mostly informative and transmit a positive attitude. The articles stress the extremely good, very close and friendly relations between MOC-OA and ROC. At the same time, the analysis shows that the Romanian Orthodox Church is considered one of the greatest supporters of the MOC on the road to its recognition and acceptance into the Orthodox world. The established close contacts contribute to the active engagement of the ROC as a lobbyist for the MOC-OA for recognizing its declared autocephality

Robust Orthogonal Complement Principal Component Analysis

Recently, the robustification of principal component analysis has attracted lots of attention from statisticians, engineers and computer scientists. In this work we study the type of outliers that are not necessarily apparent in the original observation space but can seriously affect the principal subspace estimation. Based on a mathematical formulation of such transformed outliers, a novel robust orthogonal complement principal component analysis (ROC-PCA) is proposed. The framework combines the popular sparsity-enforcing and low rank regularization techniques to deal with row-wise outliers as well as element-wise outliers. A non-asymptotic oracle inequality guarantees the accuracy and high breakdown performance of ROC-PCA in finite samples. To tackle the computational challenges, an efficient algorithm is developed on the basis of Stiefel manifold optimization and iterative thresholding. Furthermore, a batch variant is proposed to significantly reduce the cost in ultra high dimensions. The paper also points out a pitfall of a common practice of SVD reduction in robust PCA. Experiments show the effectiveness and efficiency of ROC-PCA in both synthetic and real data

Analytic Models of the ROC Curve: Applications to Credit Rating Model Validation

In this paper, the authors use the concept of the population ROC curve to build analytic models of ROC curves. Information about the population properties can be used to gain greater accuracy of estimation relative to the non-parametric methods currently in vogue. If used properly this is particularly helpful in some situations where the number of sick loans is rather small; a situation frequently met in periods of benign macro-economic background.validation; credit analysis; rating model; ROC; Basel II

Colour image processing and texture analysis on images of porterhouse steak meat

This paper outlines two colour image processing and texture analysis techniques applied to meat images and assessment of error due to the use of JPEG compression at image capture. JPEG error analysis was performed by capturing TIFF and JPEG images, then calculating the RMS difference and applying a calibration between block boundary features and subjective visual JPEG scores. Both scores indicated high JPEG quality. Correction of JPEG blocking error was trialled and found to produce minimal improvement in the RMS difference. The texture analysis methods used were singular value decomposition over pixel blocks and complex cell analysis. The block singular values were classified as meat or non- meat by Fisher linear discriminant analysis with the colour image processing result used as ‘truth.’ Using receiver operator characteristic (ROC) analysis, an area under the ROC curve of 0.996 was obtained, demonstrating good correspondence between the colour image processing and the singular values. The complex cell analysis indicated a ‘texture angle’ expected from human inspection