Outliers Detection in Multiple Circular Regression Model via DFBETAc Statistic

摘要

In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we study the relationship between more than two circular variables using the multiple circular regression model, which is proposed by [13]. The model has precise enough and interesting properties to detect the occurrence of outliers. Here, we concentrate the attention on the problem of identifying outliers in this model. In particular, the extension of DFBETAS statistic which has been successfully used for the same purpose to this model via the row deleted approach. The cut-off points and the power of performance of the procedure are investigated via Monte Carlo simulation. It is found that the performance improves when the resulting residuals have small variance and when the sample size gets larger. The real data is applied for illustration purpose.