Bayesian estimation under informative sampling

DOI10.1214/16-EJS1153zbMath1397.62117arXiv1507.07050OpenAlexW2963140874MaRDI QIDQ309532

Daniell Toth, Terrance D. Savitsky

Publication date: 7 September 2016

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1507.07050

zbMATH Keywords

Gaussian processMarkov chain Monte CarloDirichlet processsurvey samplingBayesian hierarchical modelslatent models

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Bayesian inference (62F15) Sampling theory, sample surveys (62D05)

Related Items (9)

Computationally efficient Bayesian unit-level models for non-Gaussian data under informative sampling with application to estimation of health insurance coverage ⋮ Complex sampling designs: uniform limit theorems and applications ⋮ Uncertainty Estimation for Pseudo‐Bayesian Inference Under Complex Sampling ⋮ Bayesian weighted inference from surveys ⋮ Bayesian calibration of blue crab (\textit{callinectes sapidus}) abundance indices based on probability surveys ⋮ Unnamed Item ⋮ Bayesian pairwise estimation under dependent informative sampling ⋮ Bayesian estimation under informative sampling with unattenuated dependence ⋮ Bayesian mixture modeling for multivariate conditional distributions

Uses Software

• growfunctions

Cites Work

• Unnamed Item
• Bayesian nonparametric weighted sampling inference
• Nonparametric Bayes modeling with sample survey weights
• Convergence rates of posterior distributions for non iid observations
• Convergence rates of posterior distributions.
• Probability inequalities for likelihood ratios and convergence rates of sieve MLEs
• Separable covariance arrays via the Tucker product, with applications to multivariate relational data
• Multi-level modelling under informative sampling
• Regression Analysis of Data from Complex Surveys
• Some matrix-variate distribution theory: Notational considerations and a Bayesian application
• Weighted Likelihood for Semiparametric Models and Two‐phase Stratified Samples, with Application to Cox Regression
• To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling

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