A goodness-of-fit test for parametric models based on dependently truncated data
A goodness-of-fit test for parametric models based on dependently truncated data
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基于相关截断数据的参数模型的拟合优度检验
DOI：
10.1016/j.csda.2011.12.022
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发表时间：
2012
影响因子：
1.8
通讯作者：
Takekshi Emura and Yoshihiko Konno
中科院分区：
文献类型：
--
作者：
Kentaro Kuroishi;Yoshimichi Ochi;Hideatsu Tsukahara;Takekshi Emura and Yoshihiko Konno
假设只有当 L≤X 成立时才能观察到二元随机变量 (L,X)。此类数据称为左截断数据，在许多领域都有发现，例如实验教育和流行病学。最近，人们考虑了一种在 (L,X) 上拟合参数模型的方法，该方法可以轻松地合并两个变量之间的依赖结构。参数分析的主要关注点是所施加的参数形式的拟合优度。由于相关截断模型的复杂性，传统的拟合优度程序（例如基于零分布的 Bootstrap 近似的 Kolmogorov-Smirnov 类型检验）在计算上可能不可行。在本文中，我们开发了一种计算上有吸引力且可靠的算法，用于基于渐近线性表达式的拟合优度检验。通过将乘数中心极限定理应用于渐近线性表达式，我们获得了渐近有效的拟合优度检验。蒙特卡罗模拟表明，所提出的测试具有正确的 I 类错误率和理想的经验功效。还表明，与常用的参数Bootstrap方法相比，该方法显着减少了计算时间。提供了对法学院数据的分析以供说明。补充材料中提供了用于实施拟议程序的 R 代码。
Suppose that one can observe bivariate random variables (L,X) only when L≤X holds. Such data are called left-truncated data and found in many fields, such as experimental education and epidemiology. Recently, a method of fitting a parametric model on (L,X) has been considered, which can easily incorporate the dependent structure between the two variables. A primary concern for the parametric analysis is the goodness-of-fit for the imposed parametric forms. Due to the complexity of dependent truncation models, the traditional goodness-of-fit procedures, such as Kolmogorov–Smirnov type tests based on the Bootstrap approximation to null distribution, may not be computationally feasible. In this paper, we develop a computationally attractive and reliable algorithm for the goodness-of-fit test based on the asymptotic linear expression. By applying the multiplier central limit theorem to the asymptotic linear expression, we obtain an asymptotically valid goodness-of-fit test. Monte Carlo simulations show that the proposed test has correct type I error rates and desirable empirical power. It is also shown that the method significantly reduces the computational time compared with the commonly used parametric Bootstrap method. Analysis on law school data is provided for illustration. R codes for implementing the proposed procedure are available in the supplementary material.
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影响因子：
1.8
作者：
Dietz, E;Böhning, D
通讯作者：
Böhning, D
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影响因子：
0.8
作者：
V. B. Waikar;F. J. Schuurmann;T. Raghunathan
通讯作者：
T. Raghunathan
影响因子：
2.7
作者：
HOLGATE, P
通讯作者：
HOLGATE, P
影响因子：
4.5
作者：
WOODROOFE, M
通讯作者：
WOODROOFE, M