Predictive mean matching - Misplaced Pages

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Predictive mean matching (PMM) is a widely used statistical imputation method for missing values, first proposed by Donald B. Rubin in 1986 and R. J. A. Little in 1988.

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

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

References

^ "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.

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