Outlier detection in cylindrical data / Nurul Hidayah Sadikon - Nurul Hidayah , Sadikon (2018)
A cylindrical data set consists of a circular and a linear variables. Few distributions have been proposed for such data pioneered by Johnson and Wehrly (1978). In this study, we look at two problems of detecting outliers in cylindrical data. Firstly, we define outlier in cylindrical data and propose a new test of discordancy to detect outlier in cylindrical data generated from Johnson-Wehrly distribution. Secondly, we focus on detecting outliers in Johnson-Wehrly circular-linear regression model. In both cases, the outlier detection procedures are developed using the k-nearest neighbor distance. The cut-off points are obtained and the performance of the new statistic is examined via simulation. A practical example is presented using the wind data set from the Malaysian Meteorological Department. The findings of the study should lead to better inferences, model fitting and forecasting of cylindrical data sets.