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31 results for “Outliers”
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Research ArticleOpen accessIn the abstractA 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 accessIn the abstractNote 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 accessIn the abstractSpecification 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 accessIn the abstractAn Integrated Framework for Mixed Data Clustering Using Self Organizing MapHari Prasad Devaraj, M. PunithavalliProblem statement: Clustering plays an important role in data mining of large data and helps in analysis. This develops a vast importance in research field for providing better clustering technique. There are several techniques exists for clustering the similar kind of data. But only very few techniques exists for clustering mixed data items. This leads to the requirement of better … …Journal of Computer Science, Vol. 7, Issue 11 · 18 Aug 2011 · 7,304 accessesView PDFCite
Research ArticleOpen accessIn the abstractRobust Estimations as a Remedy for Multicollinearity Caused by Multiple High Leverage PointsArezoo Bagheri, Habshah MidiProblem statement: The Least Squares (LS) method has been the most popular technique for estimating the parameters of a model due to its optimal properties and ease of computation. LS estimated regression may be seriously disturbed by multicollinearity which is a near linear dependency between two or more explanatory variables in the regression models. Even though LS estimates are unbiased … …Journal of Mathematics and Statistics, Vol. 5, Issue 4 · 31 Dec 2009 · 8,247 accessesView PDFCite
Research ArticleOpen accessIn the abstractTwo-Step Robust Diagnostic Method for Identification of Multiple High Leverage PointsArezoo Bagheri, Habshah Midi, A.H.M. Rahmatullah ImonProblem statement: High leverage points are extreme outliers in the X-direction. In regression analysis, the detection of these leverage points becomes important due to their arbitrary large effects on the estimations as well as multicollinearity problems. Mahalanobis Distance (MD) has been used as a diagnostic tool for identification of outliers in multivariate analysis where it finds the distance between normal … …Journal of Mathematics and Statistics, Vol. 5, Issue 2 · 30 Jun 2009 · 8,306 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Robust Rescaled Moment Test for Normality in RegressionMd. Sohel Rana, Habshah Midi, A.H.M. Rahmatullah ImonProblem statement: Most of the statistical procedures heavily depend on normality assumption of observations. In regression, we assumed that the random disturbances were normally distributed. Since the disturbances were unobserved, normality tests were done on regression residuals. But it is now evident that normality tests on residuals suffer from superimposed normality and often possess very poor power. Approach: This study … …Journal of Mathematics and Statistics, Vol. 5, Issue 1 · 31 Mar 2009 · 8,260 accessesView PDFCite
Research ArticleOpen accessIn the abstractFast 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 accessIn the abstractRainfall Depth-Duration-Frequency Relationships for South Carolina, North Carolina, and GeorgiaJ. P. Raiford, N.M. Aziz, A.A. Khan, D.N. PowellThe depth-duration-frequency curves and isopluvial maps for the region encompassing South Carolina, North Carolina, and Georgia were developed using the available rainfall data. The aim was to update the existing intensity-duration-frequency curves in the region and obtain these curves at ungauged sites throughout the region using the newly developed rainfall frequency analysis techniques. A total of 17 durations ranging from … …American Journal of Environmental Sciences, Vol. 3, Issue 2 · 30 Jun 2007 · 7,323 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric ModelsAbdel K.M. Baareh, Alaa F. Sheta, Khaled A. KhnaifesForecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper … …Journal of Computer Science, Vol. 2, Issue 10 · 31 Oct 2006 · 6,962 accessesView PDFCite