A comparative study of outlier detection procedures in multiple circular regression

Alkasadi, Najla Ahmed; Ibrahim, Siti Zuraidah; Ramli, Mohammad Fadzli; Yusoff, Mohd Nazaruddin

doi:10.1063/1.4965152

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…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%

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Review on Outliers Identification Methods for Univariate Circular Biological Data

Satari¹,

Khalif²

2020

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

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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.

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Section: Discussionmentioning

confidence: 99%

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

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review on Outliers Identification Methods for Univariate Circular Biological Data

Satari¹,

Khalif²

2020

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

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“…The methods used to identify abnormal observations depend on the nature of those observations. Extensive descriptions of the classification of these methods can be found in the following works: Belsley et al [58], Williams et al [59], Ben-Gal [60], and Ampanthong [61].…”

Section: Outlier Identificationmentioning

confidence: 99%

Sustainability of Income Convergence in the European Union: Two Downturns—Two Different Stories

Batóg,

Batóg

2024

Sustainability

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The aim of this paper is to apply the concept of marginal vertical income convergence to analyze the influence of the two last economic downturns (2007 and 2020) on the sustainability of the equalization of income levels within the European Union. The methodology used enables us to avoid some restrictions of the classical analysis of income convergence. Income convergence models were estimated using data from the period 1993–2022, excluding the impact of outliers. The results confirm that we can observe the progressive process of the absolute income convergence for EU members, but there are significant differences between countries’ contributions to the process. These differences are caused by different paths of economic growth, and different mean resilience to economic crises, as well as different patterns of income inequalities. Their proper recognition allows us to develop efficient policies aimed at social cohesion, reducing income inequalities (the 10th Sustainable Development Goal), and sustainable economic development. Additionally, the estimated models indicated a definite different impact of the last two economic shocks on the European process of income convergence. The first shock significantly slowed down the income convergence process, while the second one was practically neutral in this context.

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“…Natomiast propozycja wykorzystująca koncepcję reszt skorygowanych pozwala wyróżnić zarówno województwa gospodarne (kujawsko-pomorskie i pomorskie), jak i niegospodarne (łódzkie, lubelskie, podkarpackie i dolnośląskie). O tym, które podejście 1 Inne sposoby wnioskowania o nietypowości obserwacji znaleźć można na przykład w pracach [Rousseeuw, Leroy 1987;Ben-Gal 2005;Ampanthong 2009]. jest bardziej adekwatne, świadczyć może porównanie wartości klasycznych reszt i poziomu przychodów ogółem dla województw mazowieckiego i pomorskiego, dla których ta relacja wynosi odpowiednio 0,078 oraz 0,167.…”

Section: Stosowana Metodaunclassified

The Regional Effectivity of Usage of Production Factors: Analysis and Classification

Batóg¹,

Batóg²

2018

PRACE NAUKOWE UNIWERSYTETU EKONOMICZNEGO WE WROCŁAWIU

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Streszczenie: Podstawowym celem przeprowadzonego badania było dokonanie klasyfikacji polskich województw według efektywności wykorzystania czynników produkcji w postaci kapitału i siły roboczej. Identyfikacja klas województw przeprowadzona została na podstawie analizy procesów produkcyjnych zachodzących w poszczególnych regionach, z wykorzystaniem koncepcji gospodarności. Analizy dokonano w oparciu o reszty dwuczynnikowych funkcji produkcji, oszacowanych na podstawie danych przekrojowych, w okresie badawczym obejmującym lata 2007-2015. W pracy zaproponowana została również autorska modyfikacja klasycznego podejścia stosowanego w analizie gospodarności. Pozwoliła ona wyodrębnić województwa charakteryzujące się gospodarnością i niegospodarnością oraz ocenić wpływ ostatniego kryzysu gospodarczego na poziom wykorzystania czynników produkcji w skali regionalnej.

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A comparative study of outlier detection procedures in multiple circular regression

Cited by 7 publications

References 13 publications

Review on Outliers Identification Methods for Univariate Circular Biological Data

Review on Outliers Identification Methods for Univariate Circular Biological Data

Sustainability of Income Convergence in the European Union: Two Downturns—Two Different Stories

The Regional Effectivity of Usage of Production Factors: Analysis and Classification

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