[PDF] Top 20 A note on detecting statistical outliers in psychophysical data
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A note on detecting statistical outliers in psychophysical data
... Statistical outliers are observations that diverge abnormally from the overall pattern of ...of data. For example, in psychophysics, spurious data can be caused by technical error, faulty ... See full document
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A note on detecting statistical outliers in psychophysical data
... Abstract: This paper considers how best to identify statistical outliers when the underlying sampling distribution is unknown. Eight methods are described, and each is evaluated using Monte Carlo ... See full document
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Detecting and Revamping of X Outliers in Time Series Database
... abnormal data can vary considerably from a few hours to several months, and therefore, the idea of determining a proper window size in advance does not ...Many statistical tests exist [11] for ... See full document
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Detecting Outliers in High Dimensional Data Sets using Z Score Methodology
... in data that do not conform to estimate ...a statistical approach called Z-score method for outlier recognition in high-dimensional ...distant data based on data positions on ...to ... See full document
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Detecting Outliers and Influential Data Points in Receiver Operating Characteristic (ROC) Analysis
... normal outliers in the normal set of cases. Thus, we estimated statistical power by generating an observation (outlier) from a different population of normal ...rating data to obtain our outlier. ... See full document
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DISTRO: a system for detecting global outliers from distributed data streams with privacy protection
... Query Language and Query Optimization Multidatabases and Federated Databases Security and Integrity Maintenance Data Mining and Knowledge Discovery Statistical and Scientific Databases Parallel ... See full document
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Detecting Current Outliers: Continuous Outlier Detection over Time-Series Data Streams
... of data, so data mining, which identifies important information and knowledge, has become increasingly ...a data mining issue; it discovers outliers with features that differ greatly from other ... See full document
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Impact of Outliers in Data Envelopment Analysis
... the data set into two parts: a clean subset and an outlier ...clean data are used to evaluate the model ...we note to a statistical model, for instance a regression model, we can evaluate its ... See full document
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Detecting Outliers in High-Dimensional Neuroimaging Datasets with Robust Covariance Estimators
... so-called outliers, due to acquisition or preprocessing artifacts or resulting from large intrinsic inter-subject ...the statistical procedures used in group studies as the latter assume that the cohorts ... See full document
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Grid-ODF: detecting outliers effectively and efficiently in large multi-dimensional databases
... the statistical approaches that assume a distribution or probability model to fit the ...assumed data distribution) and parameter of distribution (such as mean and variance), and the expected number of ... See full document
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Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data
... of data, which substantially improves its performance on the datasets with low MI , ...StM. Note that other three coupling-based methods HOUR, CBRW and POP may ob- tain comparably good performance with SCAN ... See full document
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Detecting physically unrealistic outliers in ACE-FTS atmospheric measurements
... 2 data set is available to users in the general atmospheric community, until now no quality flags have been assigned to the ...for detecting physically unrealistic out- liers within the data set for ... See full document
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detecting outliers in web based network traffic
... external data access. While they acknowledge the importance of detecting input validation related attacks, they also note that there are a large number of techniques available to handle these kinds ... See full document
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A Robust Architecture for Detecting Outliers in IoT Data using STCPOD Model
... faulty data detection approach in wireless sensor networks based on Copula theory to ensure data ...detect outliers in wireless sensor networks using clustering and other light weight ... See full document
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Loss Functions for Detecting Outliers in Panel Data: An Introduction
... Note that the absolute difference is increasing in B (and, equivalently, in F.) If one were to use absolute difference as the measure of “outlierhood,” one would generally find that the observations with the ... See full document
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Eigenstructure-based angle for detecting outliers in multivariate data
... of outliers is very important because of its effect to the analysis ...the statistical models are simply applied to the data sets containing outliers, one might get a misleading ...of ... See full document
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REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit
... Recently, there is a renewed interest for EPP in several fields like for example hyperspectral im- agery (Malpica et al. 2008), chemistry (Hou and Wentzell 2011) and genetics (Espezua et al. 2014). In these fields, the ... See full document
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Detecting Outliers with Semi-Supervised Machine Learning : a Fraud Prediction Application
... The problem we seek to solve is the prediction of abnormalities in an environment with highly unbalanced samples and a huge mass of unlabeled data. A typical example of such a situation is provided by fraud ... See full document
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Comparative study of statistical methods for detecting association with rare variants in exome resequencing data
... family data than in the unrelated individuals data set with a few exceptions, especially the MAPT gene, for which most type I errors were biased ...individuals data set, the CA and CP approaches ... See full document
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AN APPROACH TO DETECT OUTLIERS IN OPENSTREETMAP DATA
... spatial data is increasing day by day and is greatly challenging the ability to extract useful and implicit ...spatial data mining, aims to discover those objects whose non-spatial attribute values exhibits ... See full document
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