24 research outputs found
Choral speaking: Objectives, methods, and use in the teaching of poetry
Thesis (M.A.)--Boston University, 1939. This item was digitized by the Internet Archive
Cellwise Robust M Regression
The cellwise robust M regression estimator is introduced as the first
estimator of its kind that intrinsically yields both a map of cellwise outliers
consistent with the linear model, and a vector of regression coefficients that
is robust against vertical outliers and leverage points. As a by-product, the
method yields a weighted and imputed data set that contains estimates of what
the values in cellwise outliers would need to amount to if they had fit the
model. The method is illustrated to be equally robust as its casewise
counterpart, MM regression. The cellwise regression method discards less
information than any casewise robust estimator. Therefore, predictive power can
be expected to be at least as good as casewise alternatives. These results are
corroborated in a simulation study. Moreover, while the simulations show that
predictive performance is at least on par with casewise methods if not better,
an application to a data set consisting of compositions of Swiss nutrients,
shows that in individual cases, CRM can achieve a significantly higher
predictive accuracy compared to MM regression
Plasma lipid profiles discriminate bacterial from viral infection in febrile children
Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics
Plasma lipid profiles discriminate bacterial from viral infection in febrile children
Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics
Plasma lipid profiles discriminate bacterial from viral infection in febrile children
Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar
robROSE: a robust approach for dealing with imbalanced data in fraud detection
A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. Detecting fraud in an imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected. We discuss some popular oversampling techniques that solve the problem of imbalanced data by creating synthetic samples that mimic the minority class.A frequent problem when analyzing real data is the presence of anomalies or outliers. When these atypical observations are present in the data these oversampling techniques are prone to create synthetic samples that distort the detection algorithm and spoil the resulting analysis. A useful tool for anomaly detection is robust statistics, which aims to find the outliers by first fitting the majority of the data and then flagging data observations that deviate from it.In this paper, we present a robust version of ROSE, called robROSE, which combines several promising approaches to cope simultaneously with the problem of imbalanced data and and the presence of outliers. The proposed method achieves to enhance the presence of the fraud cases while ignoring anomalies.The good performance or our new sampling technique is illustrated on simulated and real data sets and it is shown that robROSE can provide better insight in the structure of the data. The source code of the robROSE algorithm is made freely available.<br/