Thursday, Aug 7: 10:30 AM - 12:20 PM
0835
Topic-Contributed Paper Session
Music City Center
Room: CC-106C
Applied
Yes
Main Sponsor
Survey Research Methods Section
Co Sponsors
Government Statistics Section
Social Statistics Section
Presentations
Under a quasi-randomization approach to inferences from a non-probability sample, the unknown sample participation probabilities are estimated by combining the non-probability set with a reference probability sample. There could be a substantial overlap between the two sets; however, the identity of units belonging to both the non-probability and reference samples are usually unknown to the analyst. If records are not linked, approximate methods (such as, record linkage) could be applied. We study feasibility of using statistically linked sets under several scenarios of an uncertain non-probability and probability samples overlap.
Keywords
data combining
non-probability sample
participation probabilities
reference sample
We consider the problem of area-level small area modeling, and consider the efficiency and accuracy of small area predictions when privacy guarantees are desired. We consider a Bayesian framework for this problem. Here, an adversary has some information about the data and has prior opinions about the rest of the data. The parameter estimates and the small area predictions are rendered confidential in such a way such that a balance is achieved between privacy guarantees and quality of the inferences.
In this talk, I shall discuss the weak convergence of the quantile processes, which are constructed based on different estimators of the finite population quantiles under various well-known sampling designs. The results related to the weak convergence of these quantile processes are applied to find asymptotic distributions of the smooth L-estimators and the estimators of smooth functions of finite population quantiles. Based on these asymptotic distributions, confidence intervals are constructed for several finite population parameters like the median, the α-trimmed means, the interquartile range and the quantile based measure of skewness.
Keywords
High entropy sampling designs
Ratio estimator
Regression estimator
Stratified multistage cluster sampling designs
Skorohod metric
Hadamard differentiability
Sverchkov and Pfeffermann (S-P, 2023) suggested Information Criteria, similar to AIC and BIC for response model selection for Small Area Estimation under informative probability sampling of areas and within the sampled areas and not missing at random (NMAR) nonresponse. The approach considered by S-P requires knowledge of auxiliary data for complete sample before response occurs. In this paper we generalize this approach to the case where auxiliaries are not known for the non-respondents, but their totals are known.
REFERENCES
Sverchkov and Pfeffermann (2023), Response Model Selection in Small Area Estimation Under not Missing at Random Nonresponse. Calcutta Statistical Association Bulletin, pp. 1 -11
Keywords
information criteria
likelihood ratio tests
missing information principle
non-probability sampling