若干检验对非同分布随机效应模型的适用性

IF 1.2 4区数学 Q2 STATISTICS & PROBABILITY

Statistics Pub Date : 2023-03-04 DOI:10.1080/02331888.2023.2193748

D. Gaigall

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引用次数: 0

摘要

我们考虑Kolmogorov-Smirnov和cram - von- mises型检验来检验中心对称性、互换性和独立性。在标准情况下，测试的目的是让应用程序对具有未知分布的独立且相同分布的数据进行测试。测试可用于多变量数据和自举程序适用于获得临界值。我们讨论了检验对随机效应模型的适用性，其中随机效应是独立的，但不一定是相同的分布，并且可能具有未知的分布。理论结果表明，在这种情况下，试验是适当的。通过仿真研究了随机效应模型中试验的质量。得到的实证结果证实了理论结论。一个真实的数据示例说明了该应用程序。

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On the applicability of several tests to models with not identically distributed random effects

We consider Kolmogorov–Smirnov and Cramér–von-Mises type tests for testing central symmetry, exchangeability, and independence. In the standard case, the tests are intended for the application to independent and identically distributed data with unknown distribution. The tests are available for multivariate data and bootstrap procedures are suitable to obtain critical values. We discuss the applicability of the tests to random effects models, where the random effects are independent but not necessarily identically distributed and with possibly unknown distributions. Theoretical results show the adequacy of the tests in this situation. The quality of the tests in models with random effects is investigated by simulations. Empirical results obtained confirm the theoretical findings. A real data example illustrates the application.

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来源期刊

Statistics 数学-统计学与概率论

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： Statistics publishes papers developing and analysing new methods for any active field of statistics, motivated by real-life problems. Papers submitted for consideration should provide interesting and novel contributions to statistical theory and its applications with rigorous mathematical results and proofs. Moreover, numerical simulations and application to real data sets can improve the quality of papers, and should be included where appropriate. Statistics does not publish papers which represent mere application of existing procedures to case studies, and papers are required to contain methodological or theoretical innovation. Topics of interest include, for example, nonparametric statistics, time series, analysis of topological or functional data. Furthermore the journal also welcomes submissions in the field of theoretical econometrics and its links to mathematical statistics.