The Effect of Swamping on Outlier Detection in Normal Samples
نویسنده
چکیده
Background An outlier, or more descriptively an "outright liar" (Wilcoxon, 1962), is an observation (even a subset of observations) which appears to be out of line, that is, not consistent with the remainder of a data set. Such mavericks, often either innocently missed in the blind transition between data collection and computer or not so innocently slipped under the rug of "what isn't seen can't hurt", should fire the curiosity and concern of the researchers. The use of relatively small sample sizes for both hypothesis testing and parameter estimation has been well documented in the literature (e.g., Mazen, Hemmasi, & Lewis, 1987; Mazen, Graf, Kellogg, & Hemmasi, 1987; Baroudi & Orlikowski, 1989; Verma & Goodale, 1995; Mone, Mueller, & Mauland, 1996), and discordant values in small amounts of data have the potential for developing into major measurement problems.
منابع مشابه
Identification of outliers types in multivariate time series using genetic algorithm
Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...
متن کاملGeneral Foundations for Studying Masking and Swamping Robustness of Outlier Identifiers
With greatly advanced computational resources, the scope of statistical data analysis and modeling has widened to accommodate pressing new arenas of application. In all such data settings, an important and challenging task is the identification of outliers. Especially, an outlier identification procedure must be robust against the possibilities of masking (an outlier is undetected as such) and ...
متن کاملOutlier Detection in Wireless Sensor Networks Using Distributed Principal Component Analysis
Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN. The outliers in sensed data can be ca...
متن کاملApplication of Recursive Least Squares to Efficient Blunder Detection in Linear Models
In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is th...
متن کاملNonparametric Depth-Based Multivariate Outlier Identifiers, and Robustness Properties
In extending univariate outlier detection methods to higher dimension, various special issues arise, such as limitations of visualization methods, inadequacy of marginal methods, lack of a natural order, limited scope of parametric modeling, and restriction to ellipsoidal contours when using Mahalanobis distance methods. Here we pass beyond these limitations via an approach based on depth funct...
متن کامل