Predictive mean matching

Machine learning and data mining
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Predictive mean matching (PMM)^[1] is a widely used^[2] statistical imputation method for missing values, first proposed by Donald B. Rubin in 1986^[3] and R. J. A. Little in 1988.^[4]

It aims to reduce the bias introduced in a dataset through imputation, by drawing real values sampled from the data.^[5] This is achieved by building a small subset of observations where the outcome variable matches the outcome of the observations with missing values.^[1]

Compared to other imputation methods, it usually imputes less implausible values (e.g. negative incomes) and takes heteroscedastic data into account more appropriately.^[6]

References[edit]

^ ^a ^b "3.4 Predictive mean matching". stefvanbuuren.name. Retrieved 30 June 2019.
^ "Web of Science [v.5.32] – All Databases Results". apps.webofknowledge.com. Retrieved 30 June 2019.
^ Rubin, Donald B. (30 June 1986). "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations". Journal of Business & Economic Statistics. 4 (1): 87–94. doi:10.2307/1391390. JSTOR 1391390.
^ Little, Roderick J. A. (30 June 1988). "Missing-Data Adjustments in Large Surveys". Journal of Business & Economic Statistics. 6 (3): 287–296. doi:10.2307/1391878. JSTOR 1391878.
^ "Imputation by Predictive Mean Matching: Promise & Peril – Statistical Horizons". statisticalhorizons.com. Retrieved 30 June 2019.
^ "Predictive Mean Matching Imputation (Example in R)". Statistics Globe. Retrieved 2020-09-18.