Significance tests for multi-component estimands from multiply imputed, synthetic microdata

By Jerome P. Reiter. 2005

77 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/446eb805c085436a4ca994f57437c0d4d4d36ded

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Jerome P. Reiter

In Sociology and Political Science

1. Josep Domingo-Ferrer
2. Kristian Lum
3. James E. Johndrow

In Statistics and Probability

1. Jerome P. Reiter
2. Joerg Drechsler

Citations in the corpus

• Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data (2007, methodology): “In addition to improving testing for multiple imputation for missing data, the approach used to derive the denominator degrees of freedom might lead to improved testing when multiple imputation is used for protecting data confidentiality (Raghunathan et al., 2003; Reiter, 2005).”
• Using CART to generate partially synthetic public use microdata (2005, methodology): “Extensions for multivariate Q are presented in Reiter (2003c) .”
• A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, unknown): “In [64] inference on synthetic data is discussed and in [63] an application is given.”