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Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties

机译:使用自适应惩罚似然方法估计正弦模型的阶数:大样本一致性属性

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摘要

Recently, the paper introduced a method for model order estimation based on penalizing adaptively the likelihood (PAL). In this paper, we use the PAL based order estimation method for a nonlinear sinusoidal model and study its asymptotic statistical properties. We prove that the estimator of the model order using the PAL rule is consistent Simulation examples are presented to illustrate the performance of the PAL method for small sample sizes and to compare it with that of three information criterion-based methods.
机译:最近,本文介绍了一种基于自适应惩罚似然(PAL)的模型阶估计方法。在本文中,我们将基于PAL的阶估计方法用于非线性正弦模型,并研究其渐近统计特性。我们证明了使用PAL规则的模型阶估计是一致的。给出了仿真示例,以说明小样本量的PAL方法的性能,并将其与三种基于信息准则的方法进行比较。

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