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Performances Test Statistics for Single Outlier Detection in Bilinear(1,1,1,1) Models

机译:双线性(1,1,1,1)模型中单个异常值检测的性能测试统计

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摘要

An outlier detection procedure for BL(1,1,1,1) model is developed based on the maxima of the test statistics measuring the effects of IO, AO, TC and LC. A simulation study is carried out in order to investigate the sampling properties of the maxima of the outlier test statistics. It is associated with the sample size, the type of outlier and the coefficients chosen for BL(1,1,1,1). The results show that, in general, the performance of the detection procedure is good. The outlier detection procedure performs well in detecting AO for large value of (ω{sub}(AO)){top}^. As for 10, the performance of outlier detection procedure is better for model with larger coefficient values. The outlier detection procedure is capable of detecting TC and LC, though the performance is affected if ω is large.
机译:基于测量IO,AO,TC和LC影响的测试统计量的最大值,开发了BL(1,1,1,1)模型的异常检测程序。为了研究离群检验统计量最大值的采样属性,进行了模拟研究。它与样本大小,离群值类型和为BL(1,1,1,1)选择的系数相关。结果表明,总的来说,该检测程序的性能良好。离群值检测过程在针对(ω{sub}(AO)){top} ^的较大值检测AO时表现良好。对于10,离群值检测过程的性能对于系数值较大的模型更好。异常检测程序能够检测TC和LC,但是如果ω大,则会影响性能。

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