CUSUM型统计量中调节参数对变点估计效果的影响分析

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CUSUM型变点估计量中的调节参数,理论上一般假定其取值范围为(0,1),但在实际数据的变点估计时,不同的取值往往得到相异的结果.基于跳跃度变点模型,利用蒙特卡罗方法,研究了调节参数的取值对变点估计结果的影响.模拟结果发现:在跳跃度较大时,无论变点真实位置如何,变点估计值基本不受调节参数取值的影响;但在跳跃度较小时,调节参数的取值对变点估计结果有显著影响;特别在变点真实位置靠近序列其中任意一个端点时,调节参数取值为0.5时,估计的效果最好;当变点真实位置靠近中间时,调节参数的取值越小,估计效果越好.在模拟和实例分析基础上,提出了基于数据驱动的调节参数的选取方法,使得CUSUM型变点估计量更具稳健性.
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