DeepAI AI Chat
Log In Sign Up

High-dimensional imputation for the social sciences: a comparison of state-of-the-art methods

by   Edoardo Costantini, et al.
Tilburg University

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant high-dimensional MI techniques that can handle a large number of predictors in the imputation models and general missing data patterns. We assessed the relative performance of seven high-dimensional MI methods with a Monte Carlo simulation study and a resampling study based on real survey data. The performance of the methods was defined by the degree to which they facilitate unbiased and confidence-valid estimates of the parameters of complete-data analysis models. We found that using regularized regression to select the predictors used in the MI model and using principal component analysis to reduce the dimensionality of auxiliary data produce the best results.


Multiple imputation using dimension reduction techniques for high-dimensional data

Missing data present challenges in data analysis. Naive analyses such as...

Solving the "many variables" problem in MICE with principal component regression

Multiple Imputation (MI) is one of the most popular approaches to addres...

A Graph-based Imputation Method for Sparse Medical Records

Electronic Medical Records (EHR) are extremely sparse. Only a small prop...

Naive imputation implicitly regularizes high-dimensional linear models

Two different approaches exist to handle missing values for prediction: ...

Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison

Nonparametric and machine learning methods are flexible methods for obta...

Missing Values and the Dimensionality of Expected Returns

Combining 100+ cross-sectional predictors requires either dropping 90 da...