A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation
A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation
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DOI：
10.1111/biom.12149
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发表时间：
2014-06-01
期刊：
影响因子：
1.9
通讯作者：
Wang, Pei
中科院分区：
文献类型：
--
作者：
Chen, Lin S.;Prentice, Ross L.;Wang, Pei
数据缺失率可能取决于许多环境中的目标值，包括基于质谱学的蛋白质组谱研究。这里，我们考虑具有不可忽略缺失的多元高斯分布下的均值和协方差估计，其中包括响应向量的维度(P)等于或大于独立观测值(N)的情形。通过最大化一类需要显式建模缺失数据概率的惩罚似然函数，开发了一种参数估计过程。结合缺失数据机制(PEMM)估计过程的结果惩罚EM算法的性能在模拟研究和蛋白质组数据图解中被评估。
Missing data rates could depend on the targeted values in many settings, including mass spectrometry-based proteomic profiling studies. Here, we consider mean and covariance estimation under a multivariate Gaussian distribution with non-ignorable missingness, including scenarios in which the dimension (p) of the response vector is equal to or greater than the number (n) of independent observations. A parameter estimation procedure is developed by maximizing a class of penalized likelihood functions that entails explicit modeling of missing data probabilities. The performance of the resulting penalized EM algorithm incorporating missing data mechanism (PEMM) estimation procedure is evaluated in simulation studies and in a proteomic data illustration.