Outlier Detection in Multiple Circular Regression Model using DFFITc Statistic

Alkasadi, Najla Ahmed; Ibrahim, Siti Zuraidah; Abuzaid, Ali; Yusoff, Mohd Nazaruddin; Hamid, Hashibah; Leow, W.Z.; Razak, Amelia Bt Abd

doi:10.17576/jsm-2019-4807-25

Cited by 14 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, most of the outliers detection methods proposed in literature have been applied to study animal orientation data that follows Von Mises Distribution Model. Only few studies on outliers detection for biomedical data such as using spacing theory [38], and row deletion approach [8,10] are used. Therefore, more modern approaches can be explored to identify outliers in circular biological data especially in biomedical study such as 3D analysis [21], computer simulation and statistical modeling [42].…”

Section: Resultsmentioning

confidence: 99%

“…Only one biomedical data has been found recently that using univariate circular distribution model. While the other biomedical study involves multiple regression analyses for example eye data of glaucoma patients [8,9], and on circadian data which take from systolic blood pressure reading [10]. Hence, we believe there is a need to explore more on univariate circular data related to human being especially in biomedical research and health informatics since identifying outlier for univariate data is crucial in the abnormality stage investigation.…”

Section: Discussionmentioning

confidence: 99%

“…The DMCEs Statistic performed well when the sample size n and the value of concentration parameter κ are large. Other statistics called DFBETAc Statistic and COVRATIO Statistic are introduced by [8,9] applying to the eye data set. It is shown that, DFBETAc Statistic performed well and more accurate when parameters estimation become smaller after removing the outliers.…”

Section: Few Other Methods Have Been Introduced By [14] the Authors mentioning

confidence: 99%

“…Thus, a lot of study has been done to identify outliers in biological data, but most of them for circular regression model. For example on eye dataset of glaucoma patient [8,9], on circadian data taken from systolic blood pressure reading [10] and on angular of protein chain shapes [11]. Hence, there is a need to explore more outliers' detection technique for univariate circular biological data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Review on Outliers Identification Methods for Univariate Circular Biological Data

Satari¹,

Khalif²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Circular data are common in biological studies which are involved angle and direction measurements. An outlier in circular biological data mostly related to the abnormality of the data set. The existence of outliers may affect the final outcome of a data analysis. Thus, an outliers' identification method is essential in circular biological data to determine the stage of abnormality for the sample that has been studied. Past studies were mostly focusing on detecting outliers for multivariate circular biological data. However, identifying outlier for univariate data is more essential in the abnormality stage investigation. In this study, outliers' identification methods for univariate circular biological data have been reviewed. The strength and weaknesses of the methods are investigated and discussed.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Few Other Methods Have Been Introduced By [14] the Authors mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Review on Outliers Identification Methods for Univariate Circular Biological Data

Satari¹,

Khalif²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…As any other type of data, circular samples are subjected to the existence of outliers. Although the problem of outliers in univariate circular data has been well studied, 9‐11 and detection of outliers in simple and multiple circular–circular regression 12 ; there is no published work on detection of outliers in circular multivariate samples.…”

Section: Introductionmentioning

confidence: 99%

Identifying density‐based local outliers in medical multivariate circular data

Abuzaid¹

2020

Statistics in Medicine

View full text Add to dashboard Cite

This article is considered to be the first to deal with the problem of outlier‐detection in multivariate circular data. The proposed algorithm is an extension of the Local Outlier Factor (LOF) method. Two different circular distances are used; taking into account the close bounded range of circular variables, and testing all possible permutations. The performance of the algorithm is investigated via an extensive simulation study. The performance of the LOF algorithm has a direct relationship with concentration parameter, while it has an inverse relationship with the sample size. For illustrative purposes, the algorithm has been implemented on two medical multivariate circular data, namely, X‐ray beam projectors data and eye data. The extension of the LOF algorithm for other types of directional data such as spherical and cylindrical datasets is worth to be investigated.

show abstract