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- Multitask Principal Component Analysis
- The Confounding Problem of Private Data Release
- Confounding-Robust Policy Improvement
- Variational Bayesian Autoregressive Conditional Heteroskedastic Models
- Topics and Applications in Synthetic Data
- Using Permutations to Detect, Quantify and Correct for Confounding in Machine Learning Predictions
- Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity Sotiris Skaperas, Lefteris Mamatas, Arsenia Chorti
- Growing Synthetic Data Through Differentially-Private Vine Copulas
- Relative Forecasting Performance of Volatility Models: Monte Carlo Evidence by Thomas Lux and Leonardo Morales-Arias
- A Neural Stochastic Volatility Model
- Identification of Confounder in Epidemiologic Data Contaminated by Measurement Error in Covariates Paul H
- AI and Synthetic Data
- Performance Analysis of Some Machine Learning Algorithms for Regression Under Varying Spatial Autocorrelation
- Mtcopula: Synthetic Complex Data Generation Using Copula
- Tests for Conditional Heteroscedasticity with Functional Data and Goodness-Of-fit Tests for FGARCH Models
- Synthetic Data for Small Area Estimation in the American Community Survey
- Assessing Validity of Synthetic Data Inferences
- Satisfying Disclosure Restrictions with Synthetic Data Sets
- Producing Partially Synthetic Data to Avoid Disclosure Sam Hawala, U.S
- A Bayesian Synthesis Approach to Data Fusion Using
- Synthetic Data Generation for Economists
- Comparison of Deterministic Wavelet Estimation and Statistic Wavelet
- Arxiv:1907.12727V1 [Cs.LG] 30 Jul 2019
- Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
- A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis
- Gaussian Process Volatility Model
- A Synthetic Data Generator for Clustering and Outlier Analysis
- Generating Test Data for Insider Threat Detectors∗
- Generating Synthetic Data a Example Using National Pupil Dataset
- Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks
- Generating Synthetic Data in Finance: Opportunities, Challenges and Pitfalls
- Estimation of Autocorrelation Timescales with Approximate Bayesian Computations
- Model-Free Estimation of the Psychometric Function
- An Empirical Study on Principal Component Analysis for Clustering Gene Expression Data
- What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
- Augmenting Correlation Structures in Spatial Data Using Deep Generative Models
- An Automated Compatibility Prediction Engine Using DISC Theory Based Classification and Neural Networks
- Creating Synthetic Agricultural Data Sets Using Copula Techniques : I. Introduction and Background How Often Does It Happen That
- Synthetic Data Paradigm for Using and Sharing Data by Khaled El Emam and Richard Hoptroff
- Inferentially Valid, Partially Synthetic Data: Generating from Posterior Predictive Distributions Not Necessary
- The Ethics in Synthetics: Statistics in the Service of Ethics and Law in Health-Related Research in Big Data from Multiple Sources, 31 J.L
- Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data
- Learning Sparse Wavelet Representations
- Copula-Based Synthetic Data Generation for Machine Learning Emulators in Weather and Climate: Application to a Simple Radiation Model
- What Are Synthetic Data?
- Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data
- Generating Synthetic Data Through Hidden Markov Models
- Bayesian Maximum Margin Principal Component Analysis
- Inference from Fitted Models in Synthpop
- A Brief Overview of Methods for Synthetic Data for Official Statistics
- Time Series Deconfounder: Estimating Treatment Effects Over Time in the Presence of Hidden Confounders
- Invariant Representation Learning for Treatment Effect Estimation
- A Study of the Impact of Synthetic Data Generation Techniques on Data Utility Using the 1991 UK Samples of Anonymised Records
- Using Machine Learning to Assess Covid-19 Risks
- Generative Adversarial Networks for Synthetic Biomedical Signal Generation
- A Comparison of Approaches to Estimating the Time-Aggregated Uncertainty of Savings Estimated from Meter Data
- Data-Adaptive Wavelets and Multi-Scale
- STATISTICAL MODELS and ANALYSIS TECHNIQUES for LEARNING in RELATIONAL DATA Jennifer Neville University of Massachusetts Amherst
- Kernel Principal Component Analysis and Its Applications in Face Recognition and Active Shape Models
- A Pragmatic Approach to Generating Insider Threat Data
- The Psychometric Function: I. Fitting, Sampling, and Goodness of Fit
- Learning Vine Copula Models for Synthetic Data Generation
- Synthetic Data in Quantitative Scanning Probe Microscopy
- A Unified Framework for Generating Synthetic Population with Gaussian
- A Generalized Mathematical Framework for Stochastic Simulation and Forecast of Hydrologic Time Series Appendix
- Creation of Two R Shiny Applications to Illustrate and Accompany the Growclusters Package November 2019
- Mdcgen: Multidimensional Dataset Generator for Clustering
- Investigating the Use of Bayesian Networks for Small Dataset Problems Anastacia Maria Macallister Iowa State University
- An Empirical Study of Principal Component Analysis for Clustering
- Data-Driven Generation of Synthetic Load Datasets Preserving Spatio-Temporal Features
- Pavlopoulou, P. and Jones, I.F., 2020, the Influence of Source Wavelet-Estimation Error in Acoustic Time
- The Synthetic Data Vault
- Training Confounder-Free Deep Learning Models for Medical Applications
- SYNC: a Copula Based Framework for Generating Synthetic Data from Aggregated Sources