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Testing Inference in Accelerated Failure Time Models

机译:在加速故障时间模型中测试推断

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We address the issue of performing hypothesis testing in accelerated failure time models for non-censored and censored samples. The performances of the likelihood ratio test and a recently proposed test, the gradient test, are compared through simulation. The gradient test features the same asymptotic properties as the classical large sample tests, namely, the likelihood ratio, Wald and score tests. Additionally, it is as simple to compute as the likelihood ratio test. Unlike the score and Wald tests, the gradient test does require the computation of the information matrix, neither observed nor expected. Our study suggests that the gradient test is more reliable than the other classical tests when the sample is of small or moderate size.
机译:我们解决了在加速故障时间模型中表演假设检测的问题,以进行非审查和审查的样本。 通过模拟比较了似然比测试和最近提出的测试,梯度试验的性能。 梯度测试具有与经典大样本测试相同的渐近性质,即似然比,沃尔德和得分测试。 此外,计算为似然比测试是简单的。 与得分和沃尔德测试不同,梯度测试确实需要计算信息矩阵,既不观察到也没有预期。 我们的研究表明,当样品小或中等大小时,梯度测试比其他经典测试更可靠。

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