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首页> 外文期刊>Australian & New Zealand journal of statistics >Inference for short-memory time series models based on modified empirical likelihood
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Inference for short-memory time series models based on modified empirical likelihood

机译：基于修改的实证可能性的短记忆时间序列模型推断

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摘要

Empirical likelihood (EL) has been extensively studied to make statistical inferences for independent and dependent observations. However, it experiences the problem of under-coverage which causes the coverage probability of the EL-based confidence intervals to be lower than the nominal level, especially in small sample sizes. In this paper, we propose modified versions of different EL-related methods to tackle this issue, including the adjusted EL, the EL with theoretical Bartlett correction and the EL with estimated Bartlett correction for short-memory time series models. Asymptotic distributions of the likelihood-type statistics are established as the standard chi-square distribution. Simulations are conducted to compare coverage probabilities with other existing methods under different distributions. Two real data set applications demonstrate how to construct confidence regions of parameters.
机译：经验似然（EL）已被广泛研究，以对独立和依赖观察进行统计推论。然而，它经历了覆盖范围的问题，这导致基于EL的置信区间隔的覆盖率低于标称水平，特别是在小样本尺寸中。在本文中，我们提出了不同的EL相关方法的修改版本来解决这个问题，包括调整后的EL，带有理论巴特校正的EL和具有用于短存储时间序列模型的估计的Bartlett校正的EL。建立了可能性型统计的渐近分布作为标准的Chi-Square分布。进行仿真以将覆盖率与不同分布下的其他现有方法进行比较。两个真实数据集应用程序演示了如何构建参数的置信区。

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