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1. Tang, Fei. Random Forest Missing Data Approaches.

Degree: PhD, Biostatistics (Medicine), 2017, University of Miami

Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings. Currently there are many different RF imputation algorithms, but relatively little guidance about their efficacy. Using a large, diverse collection of data sets, imputation performance of various RF algorithms was assessed under different missing data mechanisms. Algorithms included proximity imputation, on the fly imputation, and imputation utilizing multivariate unsupervised and supervised splittingthe latter class representing a generalization of a new promising imputation algorithm called missForest. Our findings reveal RF imputation to be generally robust with performance improving with increasing correlation. Performance was good under moderate to high missingness, and even (in certain cases) when data was missing not at random. Real data analysis using the RF imputation methods was conducted on the MESA data. Advisors/Committee Members: Hemant Ishwaran, J. Sunil Rao, Lily Wang, Panagiota V. Caralis.

Subjects/Keywords: Random Forest; Imputation; MESA data; Missing data

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APA (6th Edition):

Tang, F. (2017). Random Forest Missing Data Approaches. (Doctoral Dissertation). University of Miami. Retrieved from

Chicago Manual of Style (16th Edition):

Tang, Fei. “Random Forest Missing Data Approaches.” 2017. Doctoral Dissertation, University of Miami. Accessed January 17, 2020.

MLA Handbook (7th Edition):

Tang, Fei. “Random Forest Missing Data Approaches.” 2017. Web. 17 Jan 2020.


Tang F. Random Forest Missing Data Approaches. [Internet] [Doctoral dissertation]. University of Miami; 2017. [cited 2020 Jan 17]. Available from:

Council of Science Editors:

Tang F. Random Forest Missing Data Approaches. [Doctoral Dissertation]. University of Miami; 2017. Available from: