使用不对称分布建模基因表达数据

Q4 Medicine

Revista Brasileira de Biometria Pub Date : 2021-06-17 DOI:10.28951/rbb.v39i2.466

Walkiria Maria de Oliveira Macerau, L. Milan

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引用次数: 0

摘要

我们简要回顾了非对称分布的稳定性，偏正态分布，偏学生t分布和偏拉普拉斯分布。我们比较了这些分布的性能，通常使用AIC和BIC来建模非对称数据。这些准则能够为每个数据集选择最佳模型。我们还将这些模型应用于基因表达数据，并验证这些分布有资格模拟这些观察结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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USING ASYMMETRIC DISTRIBUTIONS FOR MODELING GENE EXPRESSION DATA

We present a short review of the asymmetric distributions alpha-stable, skew normal, skew Student’s t and skew Laplace. We compare the performance for these distributions, in general, are used to model asymmetric data, using AIC and BIC. These criterias were able to selecting the best model for each data set. We also apply these models to gene expression data and we verify these distributions are qualified to model these observations.

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来源期刊

Revista Brasileira de Biometria Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)

自引率

0.00%

发文量

审稿时长

53 weeks