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超高维半参数纵向数据分析

布列塔尼·格林博士 连浩 于Y 祖天
生物识别技术。 八月3,2020

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随着超高维纵向数据在公共卫生和生物信息学等领域变得越来越明显,开发具有稀疏模型的灵活方法备受关注。 在这种情况下,协变量的维数可能会随着exp(𝑛关于簇数的1/2)exp(n1 / 2) n。 对于超高维纵向数据,我们考虑一种灵活的半参数方法,即部分线性单指标模型。 最重要的是,我们不仅允许部分线性协变量,而且允许未知弹性函数内的单指标协变量(非参数地估计为超高维)。 使用惩罚性广义估计方程,该方法可以捕获对象内的相关性,可以同时进行变量选择和估计,并具有平滑限幅的绝对偏差罚分,并且可以捕获非线性和预测变量之间的某些相互作用。 我们建立了针对估计器的渐进理论,包括针对部分线性和非参数分量的超高维度的oracle属性,并提出了一种有效的算法来应对计算难题。 我们通过仿真研究和酵母细胞周期基因表达数据证明了我们的方法和算法的有效性。 

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