对图基的g- &-h分布的有限混合物的估计和模型选择
Tingting Zhan1, Misung Yi1,2, Amy R Peck3
1Division of Biostatistics and Bioinformatics, Department of Pharmacology, Physiology and Cancer Biology, Thomas Jefferson University, 130 S. 9th Street, 17th Floor, Philadelphia, PA 19107 USA.
概括
这项研究介绍了灵活的Tukey.
科学领域:
- 统计建模 统计建模
- 生物统计学 生物统计学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质表达数据经常显示复杂的模式,如多模式和斜率.
- 现有的混合模型可能无法完全捕捉细胞蛋白质表达水平的多样性.
- 组织之间的变化需要强大的统计方法.
研究的目的:
- 为蛋白质表达数据提出一个灵活的有限混合模型.
- 为Tukey的g-&-h混合物引入一种新的量子最小Mahalanobis距离 (QLMD) 估计器.
- 为应用这些方法开发一个R包 (QuantileGH).
主要方法:
- 使用4参数图基的g-&-h分布对混合物成分.
- 使用量子最小马哈拉诺比斯距离 (QLMD) 来进行参数估计.
- 开发一个逐步的算法,用于节制的模型选择.
- 进行模拟研究并将模型应用于乳腺癌蛋白质表达数据.
主要成果:
- 图基的g&h混合物在模拟中表现出强大的性能.
- 建议的QLMD估计器对于非高斯混合模型是有效的.
- 该模型成功地从Cyclin D1表达数据中确定了无进展生存的预测因素.
结论:
- 图基的g-&-h混合模型为分析复杂的生物数据提供了灵活的方法.
- 量子化GHR包为研究人员提供了一个有价值的工具.
- 这种方法提高了对疾病进展中的蛋白质表达的理解.
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