Nonparametric estimation of genewise variance for microarray data

Jianqing Fan Yang Feng Yue S Niu

Data Analysis mathscidoc:1912.43403

Annals of statistics, 38, (5), 2723, 2010.11
Estimation of genewise variance arises from two important applications in microarray data analysis: selecting significantly differentially expressed genes and validation tests for normalization of microarray data. We approach the problem by introducing a two-way nonparametric model, which is an extension of the famous Neyman-Scott model and is applicable beyond microarray data. The problem itself poses interesting challenges because the number of nuisance parameters is proportional to the sample size and it is not obvious how the variance function can be estimated when measurements are correlated. In such a high-dimensional nonparametric problem, we proposed two novel nonparametric estimators for genewise variance function and semiparametric estimators for measurement correlation, via solving a system of nonlinear equations. Their asymptotic normality is established. The finite sample property is
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  title={Nonparametric estimation of genewise variance for microarray data},
  author={Jianqing Fan, Yang Feng, and Yue S Niu},
  booktitle={Annals of statistics},
Jianqing Fan, Yang Feng, and Yue S Niu. Nonparametric estimation of genewise variance for microarray data. 2010. Vol. 38. In Annals of statistics. pp.2723.
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