An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets-Reference-Cited by-同舟云学术

An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets

Published:2011-12 Issue:12 Volume:55 Page:3232-3243
ISSN:0167-9473
Container-title:Computational Statistics & Data Analysis
language:en
Short-container-title:Computational Statistics & Data Analysis

Author:

Drechsler Jörg,Reiter Jerome P.

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Statistics and Probability

Reference48 articles.

1. Abowd, J.M., Stinson, M., Benedetto, G., 2006. Final report to the social security administration on the SIPP/SSA/IRS public use file project. Technical Report. Longitudinal Employer–Household Dynamics Program, US Bureau of the Census, Washington, DC.

2. Disclosure limitation in longitudinal linked data;Abowd,2001

3. Multiply-imputing confidential characteristics and file links in longitudinal linked data;Abowd,2004

4. A training algorithm for optimal margin classifiers;Boser,1992

5. Bagging predictors;Breiman;Machine Learning,1996

Cited by 84 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. 30 Years of Synthetic Data;Statistical Science;2024-05-01

2. An evaluation of synthetic data augmentation for mitigating covariate bias in health data;Patterns;2024-04

3. An evaluation of the replicability of analyses using synthetic health data;Scientific Reports;2024-03-24

4. MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data;Data Mining and Knowledge Discovery;2024-03-22

5. Exploring the impact of financial literacy on predicting credit default among farmers: An analysis using a hybrid machine learning model;Borsa Istanbul Review;2024-03