一个基于有效确定标准的指标
Jesús E García1, Verónica A González-López1, Johsac I Gomez Sanchez1
1Department of Statistics, University of Campinas, Campinas 13083-859, São Paulo, Brazil.
本研究为分区马尔科夫模型 (PMM) 引入了新的指标,提高了模型选择的一致性. 有效的确定标准 (EDC) 提供了改进的参数惩罚,通过登革热病毒DNA序列进行验证.
科学领域:
- 计算生物学 计算生物学
- 统计建模 统计建模
- 生物信息学是一种生物信息学.
背景情况:
- 分区马尔科夫模型 (PMM) 用于序列分析.
- 贝叶斯信息标准 (BIC) 是PMM选择的常见指标.
- 现有的BIC指标可能无法保证强烈一致的估计.
研究的目的:
- 扩展基于BIC的指标,以实现强烈一致的PMM估计.
- 引入和分析PMM选择的有效确定标准 (EDC).
- 为了在EDC中确定改进的处罚功能.
主要方法:
- 将BIC泛化为有效确定标准 (EDC) 家族.
- 基于EDC指标的模型选择的正式规范.
- 对EDC的罚款的识别和验证.
- 在3型登革热病毒DNA序列建模中应用EDC指标.
主要成果:
- EDC指标提供了比BIC更广泛的参数惩罚选项.
- 在EDC内部的 ln(ln(n)) 处罚确保了非常一致的PMM估计.
- 拟议的指标证明了分析现实世界生物数据的实用性.
结论:
- EDC为PMM选择提供了一个强大的框架,并提供了更好的一致性保证.
- 对于EDC来说,罚款在理论上是合理的,但在实践中也是有用的选择.
- 这项工作推进了PMM分析,对基因组和流行病学研究有影响.
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