Research ArticleOpen accessEvery word in the titleA COMPARISON BETWEEN CLASSICAL AND ROBUST METHOD IN A FACTORIAL DESIGN IN THE PRESENCE OF OUTLIERAnwar Fitrianto, Habshah MidiAnalysis of Variance (ANOVA) techniques which is based on classical Least Squares (LS) method requires several assumptions, such as normality, constant variances and independency. Those assumptions can be violated due to several causes, such as the presence of an outlying observation. There are many evident in literatures that the LS estimate is easily affected by outliers. To remedy this problem, … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 11 Jul 2013 · 7,503 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Review on Clustering and Outlier Analysis Techniques in DataminingS. Koteeswaran, P. Visu, J. JanetProblem statement: The modern world is based on using physical, biological and social systems more effectively using advanced computerized techniques. A great amount of data being generated by such systems; it leads to a paradigm shift from classical modeling and analyses based on basic principles to developing models and the corresponding analyses directly from data. The ability to extract useful … …American Journal of Applied Sciences, Vol. 9, Issue 2 · 26 Dec 2011 · 7,372 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleNote on the Comparison of Some Outlier Labeling TechniquesN. P. OlewueziProblem statement: Methods proposed for estimating and resolving outliers are compared. Approach: In this respect, we exploit three well-known classifiers for identifying outliers to establish guidelines for the choice of outlier detection methods. Results: It was shown that the standard deviation is inappropriate to use here because it is highly sensitive to extreme values. Conclusion/Recommendations: The result of these estimated … …Journal of Mathematics and Statistics, Vol. 7, Issue 4 · 18 Nov 2011 · 8,261 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSpecification of the Outlier Univariate ModelN. P. OlewueziProblem statement: An outlier is an observation that deviates so much from other observations so as to arouse suspicion that it was generated by a different mechanism. Approach: In this study, outlier univariate model was specified based on statistical measures, Outlier Free (OF) and Outlier Contaminated (OC) models were Stated and compared. Results: The number of expected outliers obtained and … …Journal of Mathematics and Statistics, Vol. 7, Issue 4 · 3 Nov 2011 · 6,905 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSpecification of Dynamic Time Series Model with Volatile-Outlier Input SeriesT. A. Lasisi, D.K. Shangodoyin, O. O. Sangodoyin et al.Problem statement: This study considers the precision of the output series generated from aberrant input series in the context of the distribution of the dynamic estimate and also investigate relative merit of analyzing residuals with outliers for a volatility input dynamic model. Approach: The study developed a methodology for checking volatility at every time point and evaluates the influence of … …American Journal of Applied Sciences, Vol. 8, Issue 11 · 1 Oct 2011 · 7,102 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSubsample Goal Model for Multihalver on OutliersB. OnoghojobiProblem statement: In this study, a delete-half jackknife problem reformulated as a subsample multihalver was presented. Approach: In this respect, exploiting outlier nomination and estimation, since considering all possible half-sample is unpractical and unfeasible were considered. Results: We derived subsample algorithm which is unbiased multihalver and the performance of the model in formulating the subsample multihalver was shown. Conclusion: The … …Journal of Mathematics and Statistics, Vol. 6, Issue 3 · 30 Sep 2010 · 6,918 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAn Instant of Performance Criteria for Outlier IdentificationB. OnoghojobiProblem statement: Different approach to deal with criteria for multivariate outlier identification procedures when it was studentized was presented. Approach: In this respect, associated unpractical dynamic linear model and outlier identification were considered. Results: A resultant studentized Becker outlier version for multivariate outlier was obtained, as an improvement on the original model. Conclusion: The studentized Becker multivariate outlier incorporated the … …Journal of Mathematics and Statistics, Vol. 6, Issue 3 · 30 Sep 2010 · 6,991 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleFast Algorithms for Outlier DetectionFawaz A.M. Masoud, Moh'd B. Al- Zoubi, Imad Salah, Ali Al-DahoudFinding fast algorithms to detect outliers (as unusual objects) by their distance to neighboring objects is a big desire. Two algorithms were proposed to detect outliers quickly. The first was based on the Partial Distance (PD) algorithm and the second was an improved version of the PD algorithm. It was found that the proposed algorithms reduced the number of distance … …Journal of Computer Science, Vol. 4, Issue 2 · 28 Feb 2008 · 7,041 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Fast Distance-Based Algorithm to Detect OutliersMoh’d Belal Al-Zoubi, Nadim ObeidvA fast distance-based algorithm for outlier detection will be proposed. It was found that the proposed algorithm reduced the number of distance calculations compared to the nested-loop algorithm. Test results were performed on different well-known data sets. The results showed that the proposed algorithm gave a reasonable amount of CPU time saving. …Journal of Computer Science, Vol. 3, Issue 12 · 31 Dec 2007 · 6,732 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBayesian Models for Time Series with Covariates, Trend, Seasonality, Autoregression and OutliersPitsanu Tongkhow, Nantachai KantananthaBayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated time series data are used for the … …Journal of Computer Science, Vol. 9, Issue 3 · 16 Apr 2013 · 6,653 accessesView PDFCite