Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density ...

OBJECTIVE To determine the effect of spending time as an outlier (ie, an inpatient who spends time away from his or her "home" ward) on the frequency of emergency calls for patients admitted to a tertiary referral hospital. DESIGN, SETTING AND PATIENTS Observational cohort study of all patients admitted to a university-affiliated tertiary referral hospital in Melbourne, Victoria, between 1 Ju...

The presence of outliers in time series can seriously affect the model specification and parameter estimation. To avoid these adverse effects, it is essential to detect these outliers and remove them from time series. By the Bayesian statistical theory, this article proposes a method for simultaneously detecting the additive outlier (AO) and innovative outlier (IO) in an autoregressive moving-a...

Outliers as well as outlier patches, which widely emerge in dynamic process sampling data series, have strong bad influence on signal processing. In this paper, a series of recursive outlier-tolerant fitting algorithms are built to fit reliably the trajectories of a non-stationary sampling process when there are some outliers arising from output components of the process. Based on the recursive...

In this paper, an outlier detection method based on radial basis functions–principal component analysis (RBF-PCA) approach and Prescott method, a statistical detection approach, is proposed to detect the outlier in the complex system without clear mechanisms. Making full use of the capacity of neural networks on nonlinear mapping and the effect of Prescott method on outlier detection in linear ...

Four outlier detection methods are compared using both publicly available smaller statistical datasets and real-life Knowledge Discovery in Databases (KDD) datasets [1]. The smaller datasets provide insight (via visualisations) into the relative strengths and weaknesses of the compared methods. The real-life large datasets test scalability and practicality of application. We are unaware of prev...

This study describes an outlier detection technique for graph structure data that uses the centrality index. Existing techniques set thresholds for link and node regularity. However, existing techniques are not objective and do not apply to data without the link strength information. Therefore, we pay attention to centrality, which is an index used in network analysis. We perform outlier detect...

In this paper, we propose a new algorithm of removing outlier clusters. It is a voxel-based surface propagation method and can handle non-isolated and sharp featured surface outlier clusters in a fast way. Numerical experiments indicate the effectiveness of the algorithm in terms of accuracy and time efficiency. Key word: outlier removal, surface propagation

In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in dynamic systems with multiple operating points. A novel method combining the time...