Author/Authors :
Mohamed, I.B. university of malaya - Institute of Mathematical Sciences, Malaysia , Isfahani Ismail, Mohd university of malaya - Institute of Mathematical Sciences, Malaysia , Sahar Yahya, Mohd university of malaya - Centre for Foundation of Studies in Sciences, Malaysia , Ghapor Hussin, Abdul university of malaya - Centre for Foundation of Studies in Sciences, Malaysia , Noraini, Mohamed university of malaya - Centre for Foundation of Studies in Sciences, Malaysia , Zaharim, Azami Universiti Kebangsaan Malaysia - Faculty of Engineering and Built Environment, Malaysia , Said Zainol, Mohammad Universiti Teknologi MARA - Fakulti Teknologi Maklumat dan Sains Kuantitatif, Malaysia
Abstract :
This paper considers the problem of outlier detection in bilinear time series data with special focus on BL(1,0,1,1) and BL(1,1,1,1) models. In the previous study, the formulations of effect of innovational outlier on the observations and residuals from the process had been developed and the corresponding least squares estimator of outlier effect had been derived. Consequently, an outlier detection procedure employing bootstrap-based procedure to estimate the variance of the estimator had been proposed. In this paper, we proposed to use the mean absolute deviance and trimmed mean formula to estimate the variance to improve the performances of the procedure. Via simulation, we showed that the procedure based on the trimmed mean formula has successfully improved the performance of the procedure.
Keywords :
Bootstrap , bilinear , innovational outlier , least squares method
