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Article10.5351/ckss.2006.13.1.101

A Comparative Study of Microarray Data with Survival Times Based on Several Missing Mechanism

Jee-Yun Kim,Jinsoo Hwang,Seong-Sun Kim-2006-04-01-Communications for Statistical Applications and Methods
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TL;DRAbstract

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

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One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

Keywords

Missing dataImputation (statistics)Microarray analysis techniquesComputer scienceData miningProportional hazards modelStatisticsMathematics

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