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Home > Art, Film & Photography > Statistical Outliers: Outlier, Box Plot, Studentized Residual, Peirce's Criterion, Dixon's Q Test, Grubbs' Test for Outliers, Anomaly Detection
Statistical Outliers: Outlier, Box Plot, Studentized Residual, Peirce's Criterion, Dixon's Q Test, Grubbs' Test for Outliers, Anomaly Detection

Statistical Outliers: Outlier, Box Plot, Studentized Residual, Peirce's Criterion, Dixon's Q Test, Grubbs' Test for Outliers, Anomaly Detection


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About the Book

Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: Outlier, Box Plot, Studentized Residual, Peirce's Criterion, Dixon's Q Test, Grubbs' Test for Outliers, Anomaly Detection, Chauvenet's Criterion, Cook's Distance, Trimmed Estimator, Outliers Ratio, Data Quality Assurance, Outliers in Statistics. Excerpt: Anomaly Detection refers to detecting patterns in a given data set that do not conform to an established normal behavior. The patterns thus detected are called anomalies and often translate to critical and actionable information in several application domains. Anomalies are also referred to as outliers, surprise, aberrant, deviation, peculiarity, etc. Three broad categories of anomaly detection techniques exist. Supervised anomaly detection techniques learn a classifier using labeled instances belonging to normal and anomaly class, and then assign a normal or anomalous label to a test instance. Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the learnt model. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that majority of the instances in the data set are normal. Applications Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting eco-system disturbances. Popular Anomaly Detection Techniques Several anomaly detection techniques have been proposed in literature. Some of the popular techniques are: Application to Data Security Anomaly detection was proposed for Intrusion detection systems (IDS) by Dorothy Denning in 1986. Anomaly detection for IDS is normally accomplished with thr...


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Product Details
  • ISBN-13: 9781155282688
  • Publisher: Books LLC
  • Publisher Imprint: Books LLC
  • Height: 152 mm
  • Sub Title: Outlier, Box Plot, Studentized Residual, Peirce's Criterion, Dixon's Q Test, Grubbs' Test for Outliers, Anomaly Detection
  • Width: 229 mm
  • ISBN-10: 115528268X
  • Publisher Date: 02 May 2010
  • Binding: Paperback
  • Spine Width: 3 mm
  • Weight: 95 gr


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Statistical Outliers: Outlier, Box Plot, Studentized Residual, Peirce's Criterion, Dixon's Q Test, Grubbs' Test for Outliers, Anomaly Detection
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