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Rainbow plots, Bagplots and Boxplots for Functional Data

Hyndman, Rob J.; Shang, Han Lin - Department of Econometrics and Business Statistics, … - 2008

We propose new tools for visualizing large numbers of functional data in the form of smooth curves or surfaces. The proposed tools include functional versions of the bagplot and boxplot, and make use of the first two robust principal component scores, Tukey's data depth and highest density...

Persistent link: https://www.econbiz.de/10005427617

FINDING OUTLIERS AT MULTIPLE SCALES

HU, TIANMIN; SUNG, SAM YUAN - In: International Journal of Information Technology & … 04 (2005) 02, pp. 251-262

Outlier detection targets those exceptional data whose pattern is rare and lie in low density regions. In this paper, under the assumption of complete spatial randomness inside clusters, we propose an MDV (Multi-scale Deviation of the Volume) approach to identifying outliers. In addition to...

Persistent link: https://www.econbiz.de/10005033356

Effectiveness of Heuristic Based Approach on the Performance of Indexing and Clustering of High Dimensional Data

Chen, Alan; Gao, Shang; Jarada, Tamer N.; Zhang, Ming; … - In: Journal of Information & Knowledge Management (JIKM) 10 (2011) 03, pp. 261-271

utilised a number of clustering techniques, including the agglomerative hierarchical clustering, k-means clustering, and DBSCAN … attention in the research community. Indexing and clustering of high dimensional data are two of the most challenging techniques … technique applicable to indexing and clustering algorithms which need to calculate distances and check them against some minimum …

Persistent link: https://www.econbiz.de/10009274282

Robust Transformations in Univariate and Multivariate Time Series

Riani, Marco - In: Econometric Reviews 28 (2009) 1-3, pp. 262-278

It is well known that transformation of the response may improve the homogeneity and the approximate normality of the errors. Unfortunately, the estimated transformation and related test statistic may be sensitive to the presence of one, or several, atypical observations. In addition, it is...

Persistent link: https://www.econbiz.de/10005476062

Outlier Detection in GARCH Models

Doornik, Jurgen A.; Ooms, Marius - Tinbergen Instituut - 2005

We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second...

Persistent link: https://www.econbiz.de/10011257361

Extreme Value Theory Filtering Techniques for Outlier Detection

Olmo, J. - Department of Economics, City University - 2009

We introduce asymptotic parameter-free hypothesis tests based on extreme value theory to detect outlying observations in finite samples. Our tests have nontrivial power for detecting outliers for general forms of the parent distribution and can be implemented when this is unknown and needs to be...

Persistent link: https://www.econbiz.de/10011268975

Estimation of Extreme Depth-Based Quantile Regions

Einmahl, John; He, Y. - Tilburg University, Center for Economic Research - 2014

Consider the extreme quantile region, induced by the halfspace depth function HD, of the form Q = fx 2 Rd : HD(x; P) g, such that PQ = p for a given, very small p 0. This region can hardly be estimated through a fully nonparametric procedure since the sample halfspace depth is 0 outside the...

Persistent link: https://www.econbiz.de/10011090341

Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China

Wang, Jianzhou; Qin, Shanshan; Zhou, Qingping; Jiang, Haiyan - In: Renewable Energy 76 (2015) C, pp. 91-101

Interest in renewable and clean energy sources is becoming significant due to both the global energy dependency and detrimental environmental effects of utilizing fossil fuels. Therefore, increased attention has been paid to wind energy, one of the most promising sources of green energy in the...

Persistent link: https://www.econbiz.de/10011189865

Outlier Detection in Nonparametric Frontier Models

De Rock, Bram; Dehon, Catherine; Bruffaerts, Christopher - European Centre for Advanced Research in Economics and … - 2014

Persistent link: https://www.econbiz.de/10010826335

Semi-supervised outlier detection based on fuzzy rough C-means clustering

Xue, Zhenxia; Shang, Youlin; Feng, Aifen - In: Mathematics and Computers in Simulation (MATCOM) 80 (2010) 9, pp. 1911-1921

samples and fuzzy rough C-means clustering. This method introduces an objective function, which minimizes the sum squared … error of clustering results and the deviation from known labeled examples as well as the number of outliers. Each cluster is … represented by a center, a crisp lower approximation and a fuzzy boundary by using fuzzy rough C-means clustering and only those …

Persistent link: https://www.econbiz.de/10010870145