Multiple imputation in quantile regression.

An Effective Technique of Multiple Imputation in Nonparametric Quantile Regression

In this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). Multiple imputation is becoming an increasingly popular approach for analyzing missing data, which combined with quantile regression is not well-developed. We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, whi...

Multiple Imputation and Quantile Regression Methods for Biomarker Data Subject to Detection Limits

Measuring Disclosure Risk for a Synthetic Data Set Created Using Multiple Methods

Government agencies must simultaneously maintain confidentiality of individual records and disseminate useful microdata. We propose a method to create synthetic data that combines quantile regression, hot deck imputation, and rank swapping. The result from implementation of the proposed procedure is a releasable data set containing original values for a few key variables, synthetic quantile reg...

Combining Methods to Create Synthetic Microdata: Quantile Regression, Hot Deck, and Rank Swapping

Government agencies must simultaneously disseminate useful microdata and maintain confidentiality of individual records. Releasing synthetic data is one approach. We propose to create synthetic data using a combination of quantile regression, hot deck imputation, and rank swapping. The result is a releasable data set containing original values for a few key variables, synthetic quantile regress...

Quantile Regression Analysis with Missing Response, with Applications to Inequality Measures and Data Combination

We propose a quantile regression method which effectively handles missing values due to non-response. We illustrate the usefulness of our method by two examples. First example is the estimation of income inequality measures when a significant proportion of earnings are missing in survey data. Second example is when we need to combine more than two samples because no single data contains all the...