Results 151 to 160 of about 526,550 (191)

OUTLIER DIAGNOSTICS IN TIME SERIES ANALYSIS

Journal of Time Series Analysis, 1990
Abstract.Cook's likelihood displacement is a convenient measure of the impact of a model perturbation on parameter estimates. A commonly used model perturbation in regression is the deletion of acase, or equation. A natural model perturbation in the time series context is the deletion of anobservation, or a group of observations.
openaire   +3 more sources

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
openaire   +1 more source

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.
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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.
openaire   +1 more source

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 ...
openaire   +4 more sources

Outlier and influence diagnostics for meta-analysis

Research Synthesis Methods, 2010
The presence of outliers and influential cases may affect the validity and robustness of the conclusions from a meta-analysis. While researchers generally agree that it is necessary to examine outlier and influential case diagnostics when conducting a meta-analysis, limited studies have addressed how to obtain such diagnostic measures in the context of
Wolfgang, Viechtbauer, Mike W-L, Cheung
openaire   +2 more sources

Outlier Analysis: Advanced Concepts

2015
Many scenarios for outlier analysis cannot be addressed with the use of the techniques discussed in the previous chapter. For example, the data type has a critical impact on the outlier detection algorithm. In order to use an outlier detection algorithm on categorical data, it may be necessary to change the distance function or the family of ...
openaire   +1 more source

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
openaire   +1 more source