A synthetic data generation procedure for univariate circular data with various outliers scenarios using Python programming language
Synthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises (VM) distribution with various outliers scenario using Python programming...
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| Vydané v: | Journal of physics. Conference series Ročník 1988; číslo 1; s. 12111 - 12119 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Bristol
IOP Publishing
01.07.2021
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | Synthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises
(VM)
distribution with various outliers scenario using Python programming language. The procedure of formulation a synthetic data generation is proposed in this study. The synthetic data is generated from various combinations of seven sample size,
n
and five concentration parameters,
K.
Moreover, a synthetic data will be generated by formulating a data generation procedure with different condition of outliers scenarios. Three outliers scenarios are proposed in this study to introduce the outliers in synthetic dataset by placing them away from inliers at a specific distance. The number of outliers planted in the dataset are fixed with three outliers. The synthetic data is randomly generated by using Python library and package which are ‘numpy’, ‘random’ and von Mises’. In conclusion, the synthetic data of univariate circular data from von Mises distribution is generated and the outliers are successfully introduced in the dataset with three outliers scenarios using Python. This study will be valuable for those who are interested to study univariate circular data with outliers and choose Python as an analysis tool. |
|---|---|
| AbstractList | Synthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises
(VM)
distribution with various outliers scenario using Python programming language. The procedure of formulation a synthetic data generation is proposed in this study. The synthetic data is generated from various combinations of seven sample size,
n
and five concentration parameters,
K.
Moreover, a synthetic data will be generated by formulating a data generation procedure with different condition of outliers scenarios. Three outliers scenarios are proposed in this study to introduce the outliers in synthetic dataset by placing them away from inliers at a specific distance. The number of outliers planted in the dataset are fixed with three outliers. The synthetic data is randomly generated by using Python library and package which are ‘numpy’, ‘random’ and von Mises’. In conclusion, the synthetic data of univariate circular data from von Mises distribution is generated and the outliers are successfully introduced in the dataset with three outliers scenarios using Python. This study will be valuable for those who are interested to study univariate circular data with outliers and choose Python as an analysis tool. Synthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises (VM) distribution with various outliers scenario using Python programming language. The procedure of formulation a synthetic data generation is proposed in this study. The synthetic data is generated from various combinations of seven sample size, n and five concentration parameters, K. Moreover, a synthetic data will be generated by formulating a data generation procedure with different condition of outliers scenarios. Three outliers scenarios are proposed in this study to introduce the outliers in synthetic dataset by placing them away from inliers at a specific distance. The number of outliers planted in the dataset are fixed with three outliers. The synthetic data is randomly generated by using Python library and package which are ‘numpy’, ‘random’ and von Mises’. In conclusion, the synthetic data of univariate circular data from von Mises distribution is generated and the outliers are successfully introduced in the dataset with three outliers scenarios using Python. This study will be valuable for those who are interested to study univariate circular data with outliers and choose Python as an analysis tool. |
| Author | Wan Yusoff, W N S Zulkipli, N S Satari, S Z |
| Author_xml | – sequence: 1 givenname: N S surname: Zulkipli fullname: Zulkipli, N S organization: Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang , Malaysia – sequence: 2 givenname: S Z surname: Satari fullname: Satari, S Z organization: Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang , Malaysia – sequence: 3 givenname: W N S surname: Wan Yusoff fullname: Wan Yusoff, W N S organization: Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang , Malaysia |
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| Cites_doi | 10.15282/daam.v1i01.5085 10.1016/j.ins.2014.01.015 10.1080/03610918108812225 10.1080/03610918.2014.932799 10.1111/j.2517-6161.1975.tb01550.x 10.1080/02664763.2017.1342779 10.1016/S0167-9473(98)00021-8 10.25046/aj050212 |
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| DOI | 10.1088/1742-6596/1988/1/012111 |
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| References | Fisher (JPCS_1988_1_012111bib2) 1993 Mohamed (JPCS_1988_1_012111bib6) 2016; 45 Satari (JPCS_1988_1_012111bib9) 2019 Mardia (JPCS_1988_1_012111bib3) 1975; 37 Alkasadi (JPCS_1988_1_012111bib7) 2018; 13 Di (JPCS_1988_1_012111bib10) 2019; 1366 Mokhtar (JPCS_1988_1_012111bib8) 2017; 45 Philip Chen (JPCS_1988_1_012111bib11) 2014; 275 Sebert (JPCS_1988_1_012111bib13) 1998; 27 Jammalamadaka (JPCS_1988_1_012111bib1) 2001 Zulkipli (JPCS_1988_1_012111bib12) 2020; 1 Best (JPCS_1988_1_012111bib4) 1981; 10 Satari (JPCS_1988_1_012111bib5) 2020; 5 |
| References_xml | – start-page: 2059 year: 2019 ident: JPCS_1988_1_012111bib9 article-title: Single-linkage method to detect multiple outliers with different outlier scenarios in circular regression model – volume: 1 start-page: 31 year: 2020 ident: JPCS_1988_1_012111bib12 article-title: Descriptive analysis of circular data with outliers using Python programming language publication-title: Data Analytics and Applied Mathematics (DAAM) doi: 10.15282/daam.v1i01.5085 – volume: 275 start-page: 314 year: 2014 ident: JPCS_1988_1_012111bib11 article-title: Data-intensive applications, challenges, techniques and technologies: A survey on Big Data publication-title: Information Sciences doi: 10.1016/j.ins.2014.01.015 – year: 2001 ident: JPCS_1988_1_012111bib1 – volume: 10 start-page: 493 year: 1981 ident: JPCS_1988_1_012111bib4 article-title: The bias of the maximum likelihood estimators of the von Mises-Fisher concentration parameters publication-title: Communication in Statistics-Simulation and Computation doi: 10.1080/03610918108812225 – volume: 45 start-page: 2904 year: 2016 ident: JPCS_1988_1_012111bib6 article-title: A New Discordancy Test in Circular Data Using Spacings Theory publication-title: Communications in Statistics-Simulation and Computation doi: 10.1080/03610918.2014.932799 – volume: 37 start-page: 349 year: 1975 ident: JPCS_1988_1_012111bib3 article-title: Statistics of directional data publication-title: Journal of the Royal Statistical Society B. doi: 10.1111/j.2517-6161.1975.tb01550.x – volume: 13 start-page: 9083 year: 2018 ident: JPCS_1988_1_012111bib7 article-title: Outliers Detection in Multiple Circular Regression Model via DFBETAc Statistic publication-title: International Journal of Applied Engineering Research – volume: 45 start-page: 1041 year: 2017 ident: JPCS_1988_1_012111bib8 article-title: A clustering approach to detect multiple outliers in linear functional relationship model for circular data publication-title: Journal of Applied Statistics doi: 10.1080/02664763.2017.1342779 – volume: 1366 year: 2019 ident: JPCS_1988_1_012111bib10 article-title: Outlier detection in circular regression model using minimum spanning tree method publication-title: Journal of Physics: Conference Series – volume: 27 start-page: 461 year: 1998 ident: JPCS_1988_1_012111bib13 article-title: A clustering algorithm for identifying multiple outliers in linear regression publication-title: Computational Statistics and Data Analysis doi: 10.1016/S0167-9473(98)00021-8 – year: 1993 ident: JPCS_1988_1_012111bib2 – volume: 5 start-page: 95 year: 2020 ident: JPCS_1988_1_012111bib5 article-title: Review on outliers identification methods for univariate circular biological data publication-title: Advances in Science, Technology and Engineering Systems doi: 10.25046/aj050212 |
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| Title | A synthetic data generation procedure for univariate circular data with various outliers scenarios using Python programming language |
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