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- W2942893648 abstract "A cylindrical data set consists of a circular and a linear variables. Few distributions havebeen proposed for such data pioneered by Johnson and Wehrly (1978). In this study, welook at two problems of detecting outliers in cylindrical data. Firstly, we define outlierin cylindrical data and propose a new test of discordancy to detect outlier in cylindricaldata generated from Johnson-Wehrly distribution. Secondly, we focus on detecting outliersin Johnson-Wehrly circular-linear regression model. In both cases, the outlier detectionprocedures are developed using the k-nearest neighbor distance. The cut-off pointsare obtained and the performance of the new statistic is examined via simulation. Apractical example is presented using the wind data set from the Malaysian MeteorologicalDepartment. The findings of the study should lead to better inferences, model fitting andforecasting of cylindrical data sets." @default.
- W2942893648 created "2019-05-09" @default.
- W2942893648 creator A5033148807 @default.
- W2942893648 date "2018-04-01" @default.
- W2942893648 modified "2023-09-27" @default.
- W2942893648 title "Outlier detection in cylindrical data / Nurul Hidayah Sadikon" @default.
- W2942893648 hasPublicationYear "2018" @default.
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