Francesca Di Iorio, Riccardo Lucchetti, Rosaria Simone
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引用次数: 0
摘要
在本文中,我们提出了一种用于分析有序评级数据的统一和二项(CUB)组合模型的波特曼检验法(portmanteau test)。具体来说,我们建立的检验属于基于信息矩阵相等的信息矩阵(IM)检验。蒙特卡洛证据表明,在有限样本中,该检验在实际规模和功率方面相对于几种备选方案都具有出色的特性。与 IM 系列的其他检验不同,基于引导的有限样本调整似乎是不必要的。本文还提供了一个经验应用,以说明如何使用 IM 检验来补充模型验证和选择。
Testing distributional assumptions in CUB models for the analysis of rating data
In this paper, we propose a portmanteau test for misspecification in combination of uniform and binomial (CUB) models for the analysis of ordered rating data. Specifically, the test we build belongs to the class of information matrix (IM) tests that are based on the information matrix equality. Monte Carlo evidence indicates that the test has excellent properties in finite samples in terms of actual size and power versus several alternatives. Differently from other tests of the IM family, finite-sample adjustments based on the bootstrap seem to be unnecessary. An empirical application is also provided to illustrate how the IM test can be used to supplement model validation and selection.
期刊介绍:
AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.