- Tytuł:
- Methods for combining probability and nonprobability samples under unknown overlaps
- Autorzy:
-
Savitsky, Terrance D.
Williams, Matthew R.
Gershunskaya, Julie
Beresovsky, Vladislav - Powiązania:
- https://bibliotekanauki.pl/articles/31342142.pdf
- Data publikacji:
- 2023-12-07
- Wydawca:
- Główny Urząd Statystyczny
- Tematy:
-
Survey sampling
Nonprobability sampling
Data combining
Inclusion probabilities
Exact sample likelihood
Bayesian hierarchical modeling - Opis:
- Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias since the distribution of variables of interest in the convenience sample is different from the population distribution. A recent set of approaches estimates inclusion probabilities for convenience sample units by specifying reference sample-weighted pseudo likelihoods. This paper introduces a novel approach that derives the propensity score for the observed sample as a function of inclusion probabilities for the reference and convenience samples as our main result. Our approach allows specification of a likelihood directly for the observed sample as opposed to the approximate or pseudo likelihood. We construct a Bayesian hierarchical formulation that simultaneously estimates sample propensity scores and the convenience sample inclusion probabilities. We use a Monte Carlo simulation study to compare our likelihood based results with the pseudo likelihood based approaches considered in the literature.
- Źródło:
-
Statistics in Transition new series; 2023, 24, 5; 1-34
1234-7655 - Pojawia się w:
- Statistics in Transition new series
- Dostawca treści:
- Biblioteka Nauki
Artykuł