二维指数信号模型中参数Bootstrap估计的强相合性

Strong Consistency of Parameters' Bootstrap Estimators in Two-Dimensional Exponential Signal Model

  • 摘要: 本文主要研究了在统计信号处理当中具有广泛应用的二维带白噪声指数信号模型中参数估计的Bootstrap逼近, 借助于回归模型中Bootstrap逼近的构造方法, 给出了二维指数信号模型参数的自助估计, 并证明了自助估计具有强相合性. 最后采用了Monte-Carlo法对所提的方法进行随机模拟, 模拟的结果表明当噪声不服从正态分布时, Bootstrap方法的估计效果优于最小二乘估计.

     

    Abstract: In this paper, we investigate the Bootstrap approximation for two-dimensional exponential signal model with noise, which has a wide applications in statistical signal processing, and give the Bootstrap estimators of the parameters by virtue of the construction method of Bootstrap approximation in regressive model and prove the strong consistency. By Monte-carlo simulation, the results indicate that Bootstrap estimators are better than Least-squares estimators when the noise do not obey the normal distribution.

     

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