Fully Bayesian estimation under informative sampling

@article{LenNovelo2019FullyBE,
  title={Fully Bayesian estimation under informative sampling},
  author={Luis G. Le{\'o}n-Novelo and Terrance D. Savitsky},
  journal={Electronic Journal of Statistics},
  year={2019}
}
Bayesian estimation is increasingly popular for performing model based inference to support policymaking. These data are often collected from surveys under informative sampling designs where subject inclusion probabilities are designed to be correlated with the response variable of interest. Sampling weights constructed from marginal inclusion probabilities are typically used to form an exponentiated pseudo likelihood that adjusts the population likelihood for estimation on the sample due to… Expand

Figures and Tables from this paper

Bayesian Estimation Under Informative Sampling with Unattenuated Dependence
TLDR
This work shows that the pseudo-posterior is consistent not only for survey designs which have asymptotic factorization, but also for designs with residual or unattenuated dependence, and establishes a broad class of analysis models that can be applied to a wide variety of survey data sets. Expand
Bayesian Uncertainty Estimation Under Complex Sampling
TLDR
Through insights from survey sampling variance estimation and recent advances in computational methods, this work devise a correction applied as a simple and fast post-processing step to MCMC draws of the pseudo-posterior distribution that projects the Pseudo-Posterior covariance matrix such that the nominal coverage is approximately achieved. Expand
Fully Bayesian Estimation under Dependent and Informative Cluster Sampling
Funding information Survey data are often collected under multistage sampling designs where units are binned to clusters that are sampled in a first stage. The unit-indexed population variables ofExpand
Conjugate Bayesian unit‐level modelling of count data under informative sampling designs
TLDR
This work develops a unit-level model for count data that accounts for informative sampling designs and includes fully Bayesian model uncertainty propagation and holds under the pseudo-likelihood, yielding an extremely computationally efficient approach. Expand
Unit Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling: A Comprehensive Overview with Extensions
Model-based small area estimation is frequently used in conjunction with survey data in order to establish estimates for under-sampled or unsampled geographies. These models can be specified atExpand
Submitted to the Annals of Statistics COMPLEX SAMPLING DESIGNS : UNIFORM LIMIT THEOREMS AND APPLICATIONS By Qiyang
In this paper, we develop a general approach to proving global and local uniform limit theorems for the Horvitz-Thompson empirical process arising from complex sampling designs. Global theorems suchExpand
Private Tabular Survey Data Products through Synthetic Microdata Generation
We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights byrecord likelihoodExpand
A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions
TLDR
A Bayesian logistic regression model identified a strong association of RA with multiple second- and third-order interactions, many of which involve age or BMI as one of the factors, suggesting a potential role of risk-factor interactions in RA disease mechanism. Expand

References

SHOWING 1-10 OF 17 REFERENCES
Bayesian Estimation Under Informative Sampling
Bayesian analysis is increasingly popular for use in social science and other application areas where the data are observations from an informative sample. An informative sampling design leads toExpand
Bayesian Nonparametric Weighted Sampling Inference
TLDR
A hierarchical Bayesian approach is proposed in which the weights of the nonsampled units in the population are model and simultaneously include them as predictors in a nonparametric Gaussian process regression to yield valid inference for the underlying nite population and capture the uncertainty induced by sampling and the unobserved outcomes. Expand
Bayesian pseudo-empirical-likelihood intervals for complex surveys
Bayesian methods for inference on finite population means and other parameters by using sample survey data face hurdles in all three phases of the inferential procedure: the formulation of aExpand
A nonparametric method to generate synthetic populations to adjust for complex sampling design features.
TLDR
A method to generate synthetic populations from a posterior predictive distribution in a fashion inverts the complex sampling design features and generates simple random samples from a superpopulation point of view. Expand
Nonparametric Bayes modeling with sample survey weights.
TLDR
This work proposes a simple approach based on modeling the distribution of the selected sample as a mixture, with the mixture weights appropriately adjusted, while accounting for uncertainty in the adjustment. Expand
Multi-level modelling under informative sampling
We consider a model-dependent approach for multi-level modelling that accounts for informative probability sampling of first- and lower-level population units. The proposed approach consists of firstExpand
PARAMETRIC DISTRIBUTIONS OF COMPLEX SURVEY DATA UNDER INFORMATIVE PROBABILITY SAMPLING
The sample distribution is defined as the distribution of the sample mea- surements given the selected sample. Under informative sampling, this distribution is different from the correspondingExpand
To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling
Finite population sampling is perhaps the only area of statistics in which the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measuredExpand
Fully Bayesian spline smoothing and intrinsic autoregressive priors
There is a well-known Bayesian interpretation for function estimation by spline smoothing using a limit of proper normal priors. The limiting prior and the conditional and intrinsic autoregressiveExpand
Model Assisted Survey Sampling
TLDR
This book presents the principles of Estimation for Finite Populations and Important Sampling Designs and a Broader View of Errors in Surveys: Nonsampling Errors and Extensions of Probability Sampling Theory. Expand
...
1
2
...