Multiple imputation with sparse kernel machines

Multiple imputation is a popular and flexible method for facilitating inferential modeling in the presence of missing data. Issues with the dimensions and characteristics of larger data sets hamper the effectiveness of traditional multiple imputation methods and may instead be alleviated using sparse kernel machine methods. A series of Monte Carlo simulation studies was conducted in order to ascertain the effectiveness of sparse kernel machines as multiple imputation methods in comparison to traditional approaches.