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Confidence intervals based on the deviance statistic for the hyperparameters in state space models

机译:基于国家空间模型中的超参数的偏差统计的置信区间

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The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.
机译:这项工作的主要目标是评估利用偏差统计建造的置信区间的性能,用于状态空间模型的超参数。基于可能性测试,第一过程是对置信区的边缘近似,并且第二个过程基于符号根偏差轮廓。这些方法是计算有效的,并且不受任何问题的影响,例如参数空间外部的间隔,这可能是焦点在误差的差异时的情况。将该程序与文献中存在的通常方法进行比较,其包括基于最大似然估计器的渐近分布的方法,以及引导置信区间。通过蒙特卡罗研究进行比较,以便确定每个方法的优点和缺点。结果表明,基于偏差统计的方法具有比渐近和引导程序更好的覆盖率。

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