Pretest estimation in combining probability and non-probability samples
From MaRDI portal
Publication:6170605
DOI10.1214/23-ejs2137arXiv2305.17801OpenAlexW4379802332MaRDI QIDQ6170605
Publication date: 10 August 2023
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2305.17801
Asymptotic distribution theory in statistics (62E20) Parametric hypothesis testing (62F03) Robustness and adaptive procedures (parametric inference) (62F35) Sampling theory, sample surveys (62D05)
Cites Work
- Unnamed Item
- Unnamed Item
- Dynamic treatment regimes: technical challenges and applications
- Statistical data integration in survey sampling: a review
- Asymptotic results for multiple imputation
- Pseudo-likelihood and quasi-likelihood estimation for complex sampling schemes
- Model assisted survey sampling.
- Inference for nonprobability samples
- Fixed effects, random effects or Hausman-Taylor: a pretest estimator
- On making valid inferences by integrating data from surveys and other sources
- Semiparametric theory and missing data.
- Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m ‐Out‐of‐ n Bootstrap Scheme
- Essential Statistical Inference
- Adaptive Confidence Intervals for the Test Error in Classification
- Sampling Statistics
- The central role of the propensity score in observational studies for causal effects
- Models for Nonresponse in Sample Surveys
- Calibration Estimators in Survey Sampling
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Bootstrap Sample Size in Nonregular Cases
- Instrumental Variables Regression with Weak Instruments
- Asymptotic Statistics
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
- Modelling Overdispersion for Complex Survey Data
- Doubly Robust Inference when Combining Probability and Non-Probability Samples with High Dimensional Data
- Combining Multiple Observational Data Sources to Estimate Causal Effects
- Doubly Robust Inference With Nonprobability Survey Samples
- Optimal Structural Nested Models for Optimal Sequential Decisions
- Bias-Reduced Doubly Robust Estimation
- Doubly robust inference with missing data in survey sampling
- Bootstrap methods for imputed data from regression, ratio and hot‐deck imputation
- Elliptical and Radial Truncation in Normal Populations
- A Simplex Method for Function Minimization
- Relations between Weak and Uniform Convergence of Measures with Applications
- Big Data, Official Statistics and Some Initiatives by the Australian Bureau of Statistics
- Developments in Survey Research over the Past 60 Years: A Personal Perspective
- Sampling Techniques for Big Data Analysis
This page was built for publication: Pretest estimation in combining probability and non-probability samples