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On hypothesis testing inference in location-scale models under model misspecification

机译:关于模型拼写下的位置尺度模型的假设检测推断

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The likelihood ratio, Wald, score and gradient test statistics can result in misleading conclusions when the assumed parametric model to the data at hand is not correctly specified. To overcome this issue, robust versions of these test statistics have been proposed in the statistic literature under model misspecification. In this paper, we address the issue of performing hypothesis testing inference in location-scale models under model misspecification. Monte Carlo simulation experiments are carried out to verify the performance of the robust test statistics, as well as usual test statistics (i.e. non-robust), in the class of location-scale models under model misspecification. The simulation results reveal that the robust tests we propose are more reliable than the usual tests since they lead to an accurate inference. An empirical application to real data is considered for illustrative purposes.
机译:当未正确指定手头的数据的假定参数模型时,可能会导致误导性比率,导致错误的结论。为了克服这个问题,已经在模型拼写下的统计文献中提出了这些测试统计数据的强大版本。在本文中,我们解决了模型误操作下的位置规模模型中对实验测试推断进行了假设检测的问题。蒙特卡罗仿真实验是进行的,以验证强大的测试统计数据的性能,以及通常的测试统计(即非鲁棒),在模型拼写的位置规模模型中。仿真结果表明,我们提出的稳健测试比通常的测试更可靠,因为它们导致准确推理。对实际数据的经验应用被认为是为了说明目的。

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