Multiple Contrast Tests in the Presence of Partial Heteroskedasticity

IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Mario Hasler, Tim Birr, Ludwig A. Hothorn
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引用次数: 0

Abstract

This paper proposes a general approach for handling multiple contrast tests for normally distributed data in the presence of partial heteroskedasticity. In contrast to the usual case of complete heteroskedasticity, the treatments belong to subgroups according to their variances. Treatments within these subgroups are homoskedastic, whereas treatments of different subgroups are heteroskedastic. New candidate as well as already existing approaches are described and compared by α $\alpha$ -simulations. Power simulations show that a gain in power is achieved when the partial heteroskedasticity is taken into account compared to procedures which wrongly assume complete heteroskedasticity. The new approaches will be applied to a phytopathological experiment.

Abstract Image

部分异方差存在下的多重对比检验。
本文提出了一种处理存在部分异方差的正态分布数据的多重对比检验的一般方法。与通常情况下的完全异方差相反,治疗根据其方差属于亚组。这些亚组内的处理是同方差的,而不同亚组的处理是异方差的。通过α $\ α $模拟对新的候选方法和现有方法进行了描述和比较。功率仿真表明,与错误地假设完全异方差的方法相比,考虑部分异方差的方法可以获得功率增益。新方法将应用于植物病理学实验。
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来源期刊
Biometrical Journal
Biometrical Journal 生物-数学与计算生物学
CiteScore
3.20
自引率
5.90%
发文量
119
审稿时长
6-12 weeks
期刊介绍: Biometrical Journal publishes papers on statistical methods and their applications in life sciences including medicine, environmental sciences and agriculture. Methodological developments should be motivated by an interesting and relevant problem from these areas. Ideally the manuscript should include a description of the problem and a section detailing the application of the new methodology to the problem. Case studies, review articles and letters to the editors are also welcome. Papers containing only extensive mathematical theory are not suitable for publication in Biometrical Journal.
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