Results 271 to 280 of about 23,619,888 (317)

DAMAGE DETECTION USING OUTLIER ANALYSIS

Journal of Sound and Vibration, 2000
Abstract This paper constitutes a study of a statistical method for damage detection. The lowest level of fault detection is considered so that the methods are simply required to signal deviations from normal condition; i.e., the problem is one of novelty detection.
K. WORDEN, G. MANSON, N.R.J. FIELLER
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Strategies for outlier analysis

IEE Two-day Colloquium on Knowledge Discovery and Data Mining, 1998
The handling of anomalous or outlying observations in a data set is one of the most important tasks in data pre-processing. It is important for three reasons. First, outlying observations can have a considerable influence on the results of an analysis.
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Isotopic Ratio Outlier Analysis (IROA) for Quantitative Analysis

2019
There is always a tension within the omics sciences between trying to measure biological molecules rapidly and measuring accurately. Metabolomics as an omics science tries to measure the small biochemicals rapidly, in a single pass, but the current state of the art cannot provide the reproducibility or accuracy needed for clinical use or even daily ...
Chris, Beecher, Felice A, de Jong
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Application of outlier sample analysis

Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, 2011
In order to optimize calibration set and increase prediction accuracy of the calibration model when near infrared spectroscopy was used to develop the model for rice amylose content, 18 abnormal spectrums produced by subjective and objective factors were eliminated based on Mahalanobis distance criterion combined with prediction concentration residual ...
null Xingang Xie, null Lijuan Shi
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Applications of Outlier Analysis

2012
Outlier analysis has numerous applications in a wide variety of domains, such as the financial industry, quality control, fault diagnosis, intrusion detection, Web analytics, and medical diagnosis. The applications of outlier analysis are so diverse that it is impossible to exhaustively cover all possibilities in a single chapter.
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Cointegration analysis in the presence of outliers [PDF]

The Econometrics Journal, 2004
Summary: The effects of innovational outliers and additive outliers in cointegrated vector autoregressive models are examined and it is analyzed how outliers can be modelled with dummy variables. A Monte Carlo simulation illustrates that additive outliers are more distortionary than innovational outliers, and misspecified dummies may distort inference ...
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Cluster Analysis for Outlier Detection

2009
For many applications in knowledge discovery in databases finding outliers, rare events, is of importance. Outliers are observations, which deviate significantly from the rest of the data, so that it seems they are generated by another process (Hawkins, 1980).
Frank Klawonn, Frank Rehm
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Outliers in Microarray Data Analysis

2006
This paper presents a broad survey of analysis methods that have been found useful in the detection and treatment of outliers, or “anomalous data points,” that often arise in large datasets. One source of these data anomalies is method outliers like “gross measurement errors” that are of no inherent biological interest, but a second source is ...
Ronald K. Pearson, Gregory E. Gonye, James S. Schwaber   +2 more
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Outlier Detection in Cointegration Analysis

Journal of Business & Economic Statistics, 1998
Franses, Philip Hans, Lucas, A (André)
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An Introduction to Outlier Analysis

2012
Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data mining and statistics literature. In most applications, the data is created by one or more generating processes, which could either reflect activity in the system or observations collected about entities.
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