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The Impact of Outliers on Parameter Estimation Method for Bilinear Model
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Nonlinear least squares (NLS) method along with Newton-Raphson (NR) iterative procedure is the best method to estimate parameters for bilinear model. However, the existence of outliers will affect the estimated value of the parameter and its validity can be doubtful. This statement was proven by conducting simulation analysis for the bilinear model, especially on bilinear (1,0,1,1) model without and with the existence of additive outlier (AO), innovational outlier (IO), temporary change (TC) and level change (LC) in the data. The performance of the NLS method is measured in terms of bias. Numerical results show that, in general, the NLS method performs better in estimating the parameters without the existence of AO, IO, TC or LC in the data.
Keywords: bilinear model; nonlinear least squares; Newton-Raphson; additive outlier; innovational outlier; temporary change; level change
Academy of Sciences Malaysia
Title: The Impact of Outliers on Parameter Estimation Method for Bilinear Model
Description:
Nonlinear least squares (NLS) method along with Newton-Raphson (NR) iterative procedure is the best method to estimate parameters for bilinear model.
However, the existence of outliers will affect the estimated value of the parameter and its validity can be doubtful.
This statement was proven by conducting simulation analysis for the bilinear model, especially on bilinear (1,0,1,1) model without and with the existence of additive outlier (AO), innovational outlier (IO), temporary change (TC) and level change (LC) in the data.
The performance of the NLS method is measured in terms of bias.
Numerical results show that, in general, the NLS method performs better in estimating the parameters without the existence of AO, IO, TC or LC in the data.
Keywords: bilinear model; nonlinear least squares; Newton-Raphson; additive outlier; innovational outlier; temporary change; level change.
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