On the public citation graph
Nonparametric Generation of Synthetic Data Using Copulas
0 signed · 55 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9781119196037openalex-snapshot
Nonparametric Statistical Methods
inferredcites →https://doi.org/10.1007/s11263-020-01365-4openalex-snapshot
Pixel-Wise Crowd Understanding via Synthetic Data
inferredcites →https://doi.org/10.1016/j.compbiomed.2022.105916openalex-snapshot
Application of data augmentation techniques towards metabolomics
inferredcites →https://doi.org/10.1016/j.dss.2009.05.016openalex-snapshot
Modeling wine preferences by data mining from physicochemical properties
inferredcites →https://doi.org/10.1016/j.eswa.2017.09.030openalex-snapshot
Effective data generation for imbalanced learning using conditional generative adversarial networks
inferredcites →https://doi.org/10.1016/j.jneumeth.2020.108885openalex-snapshot
Data augmentation for deep-learning-based electroencephalography
inferredcites →https://doi.org/10.1016/j.jtrangeo.2020.102902openalex-snapshot
A statistical approach to small area synthetic population generation as a basis for carless evacuation planning
inferredcites →https://doi.org/10.1016/j.measen.2021.100225openalex-snapshot
The application of synthetic data generation and data-driven modelling in the development of a fraud detection system for fuel bunkering
inferredcites →https://doi.org/10.1016/j.mlwa.2022.100361openalex-snapshot
Predicting severely imbalanced data disk drive failures with machine learning models
inferredcites →https://doi.org/10.1016/j.mlwa.2022.100375openalex-snapshot
Solving the class imbalance problem using a counterfactual method for data augmentation
inferredcites →https://doi.org/10.1016/j.neucom.2019.12.136openalex-snapshot
Generation and evaluation of privacy preserving synthetic health data
inferredcites →https://doi.org/10.1055/s-0042-1760247openalex-snapshot
Synthetic Tabular Data Evaluation in the Health Domain Covering Resemblance, Utility, and Privacy Dimensions
inferredcites →https://doi.org/10.1109/access.2022.3144765openalex-snapshot
A Multi-Dimensional Evaluation of Synthetic Data Generators
inferredcites →https://doi.org/10.1109/icdmw51313.2020.00082openalex-snapshot
SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources
inferredcites →https://doi.org/10.1109/jbhi.2020.2980262openalex-snapshot
Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN)
inferredcites →https://doi.org/10.1109/msp.2017.2765202openalex-snapshot
Generative Adversarial Networks: An Overview
inferredcites →https://doi.org/10.1109/tgrs.2020.3016086openalex-snapshot
Road Segmentation for Remote Sensing Images Using Adversarial Spatial Pyramid Networks
inferredcites →https://doi.org/10.1109/tsg.2021.3061395openalex-snapshot
Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids
inferredcites →https://doi.org/10.1145/3548785.3548793openalex-snapshot
Synthetic Data Generation: A Comparative Study
inferredcites →https://doi.org/10.1201/9781315140919openalex-snapshot
Density Estimation for Statistics and Data Analysis
inferredcites →https://doi.org/10.1214/aos/1018031260openalex-snapshot
Multivariate analysis by data depth: descriptive statistics, graphics and inference, (with discussion and a rejoinder by Liu and Singh)
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.2196/18910openalex-snapshot
Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing
inferredcites →https://doi.org/10.3390/electronics11010060openalex-snapshot
A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation
inferredcites →https://doi.org/10.3390/electronics11050812openalex-snapshot
Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain
inferredcites →https://doi.org/10.3390/electronics11060837openalex-snapshot
MaWGAN: A Generative Adversarial Network to Create Synthetic Data from Datasets with Missing Data
inferredcites →https://doi.org/10.3390/electronics11203277openalex-snapshot
Statistical Validation of Synthetic Data for Lung Cancer Patients Generated by Using Generative Adversarial Networks
inferredcites →https://doi.org/10.3390/electronics9111892openalex-snapshot
Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems
inferredcites →https://doi.org/10.3390/sym13071176openalex-snapshot
Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-032-15990-8_2openalex-snapshot
A Multi-Dimensional Comparative Study of Generative Adversarial Networks, Diffusion Models, and Statistical Methods for Synthetic Health Data Generation
inferred← citeshttps://doi.org/10.1007/978-3-032-30863-4_13openalex-snapshot
Ensuring Data Privacy and Utility in Synthetic Data Generation for Analysis of International Student Admissions Data
inferred← citeshttps://doi.org/10.1007/978-3-032-37777-7_32openalex-snapshot
Learning Joint Event Attribute Distributions for Realistic Event Log Generation
inferred← citeshttps://doi.org/10.1007/s10845-026-02904-5openalex-snapshot
Synthetic process modeling for ring rolling via physics-guided GANs and analytical transfer learning
inferred← citeshttps://doi.org/10.1007/s10922-025-10026-1openalex-snapshot
SereWay: Toward SEcurity and REliability Benchmarking for the RailWAY IIoT
inferred← citeshttps://doi.org/10.1007/s12289-026-01991-2openalex-snapshot
Machine learning prediction of the forming limit curve of dual phase steels
inferred← citeshttps://doi.org/10.1016/j.asej.2024.103060openalex-snapshot
Synthetic data generation using Copula model and driving behavior analysis
inferred← citeshttps://doi.org/10.1016/j.seps.2026.102557openalex-snapshot
Balancing the scales: Fair and efficient resource allocation for student success
inferred← citeshttps://doi.org/10.1038/s41598-025-26228-1openalex-snapshot
Data augmentation alters feature importance in XGBoost for CVD prediction
inferred← citeshttps://doi.org/10.1109/access.2025.3603435openalex-snapshot
Quality Data Extractor (QDE): Elevating Synthetic Data Augmentation Through Post-Generation Filtration
inferred← citeshttps://doi.org/10.1109/access.2025.3619091openalex-snapshot
Generative Private Synthetic Student Data for Learning Analytics: An Empirical Study
inferred← citeshttps://doi.org/10.1109/access.2025.3642197openalex-snapshot
Synthetic Data Generation for Microalgal Cultivation Using Variational Autoencoders, GANs, and Copula-Based Models: A Case Study on Nannochloropsis Oculata
inferred← citeshttps://doi.org/10.1109/bigdata62323.2024.10826070openalex-snapshot
SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
inferred← citeshttps://doi.org/10.1109/cbms65348.2025.00094openalex-snapshot
Differentially Private Non Parametric Copulas: Generating Synthetic Data with Non Parametric Copulas Under Privacy Guarantees
inferred← citeshttps://doi.org/10.1109/iwcmc65282.2025.11059481openalex-snapshot
Open-FARI: An Open-source testbed for Federated Anomaly detection in the Railway Industrial Internet of Things
inferred← citeshttps://doi.org/10.1177/00368504251366850openalex-snapshot
Enhancing body fat prediction with WGAN-GP data augmentation and XGBoost algorithm
inferred← citeshttps://doi.org/10.3389/fdgth.2025.1576290openalex-snapshot
Comprehensive evaluation framework for synthetic tabular data in health: fidelity, utility and privacy analysis of generative models with and without privacy guarantees
inferred← citeshttps://doi.org/10.3390/buildings15203799openalex-snapshot
A Framework for Evaluating Cost Performance of Architectural Projects Using Unstructured Data and Random Forest Model Focusing on Korean Cases
inferred← citeshttps://doi.org/10.3390/engproc2026150050openalex-snapshot
Inverse Copula Sampling for Multi-Dimensional Data Synthesis
inferred← citeshttps://doi.org/10.64898/2026.08.03.742474openalex-snapshot
Synthetic Longitudinal Tabular Data Generation via Copula
inferred← citeshttps://doi.org/10.64898/2026.08.31.26361878openalex-snapshot
Evaluating synthetic-data fidelity in two-group biomedical studies: a multidimensional validation framework
inferred← citeshttps://doi.org/10.7717/peerj-cs.3228openalex-snapshot
Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.