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Bootstrap Bartlett correction in inflated beta regression

机译:膨胀的beta回归中的Bootstrap Bartlett校正

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摘要

The inflated beta regression model aims to enable the modeling of responses in the intervals (0, 1], [0, 1), or [0, 1]. In this model, hypothesis testing is often performed based on the likelihood ratio statistic. The critical values are obtained from asymptotic approximations, which may lead to distortions of size in small samples. In this sense, this article proposes the bootstrap Bartlett correction to the statistic of likelihood ratio in the inflated beta regression model. The proposed adjustment only requires a simple Monte Carlo simulation. Through extensive Monte Carlo simulations the finite sample performance (size and power) of the proposed corrected test is compared to the usual likelihood ratio test and the Skovgaard adjustment already proposed in the literature. The numerical results evidence that inference based on the proposed correction is much more reliable than that based on the usual likelihood ratio statistics and the Skovgaard adjustment. At the end of the work, an application to real data is also presented.
机译:膨胀的beta回归模型旨在对间隔(0,1],[0,1)或[0,1]中的响应进行建模。在此模型中,通常基于似然比统计量进行假设检验。临界值是从渐近近似中获得的,这可能会导致小样本中尺寸的失真。从这个意义上讲,本文提出了对夸张的β回归模型中似然比统计量的Bootstrap Bartlett校正。提出的调整仅需要简单的蒙特卡洛模拟。通过广泛的蒙特卡洛模拟,将所提出的校正测试的有限样本性能(大小和功效)与文献中已经提出的通常似然比测试和Skovgaard调整进行了比较。数值结果表明,基于所提出的校正的推断比基于通常的似然比统计和Skovgaard调整的推断更加可靠。在工作的最后,还介绍了实际数据的应用程序。

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