arXiv subjects

Vladislav Beresovsky

Publications and source records attributed to Vladislav Beresovsky.

5 recordsLinked to original sources

arXiv2025

Thresholding Nonprobability Units in Combined Data for Efficient Domain Estimation

Quasi-randomization approaches estimate latent participation probabilities for units from a nonprobability / convenience sample. Estimation of participation probabilities for convenience units allows their combination with units from the randomized survey sample to form a survey weighted domain estimate. One leverages convenience units for domain estimation under the expectation that estimation precision and bias will improve relative to solely using the survey sample; however, convenience sample units that are very different in their covariate support from the survey sample units may inflate estimation bias or variance. This paper develops a method to threshold or exclude convenience units to minimize the variance of the resulting survey weighted domain estimator. We compare our thresholding method with other thresholding constructions in a simulation study for two classes of datasets based on degree of overlap between survey and convenience samples on covariate support. We reveal that excluding convenience units that each express a low probability of appearing in \emph{both} reference and convenience samples reduces estimation error.

stat.ME↗

arXiv2023

Review of Quasi-Randomization Approaches for Estimation from Non-probability Samples

The recent proliferation of computers and the internet have opened new opportunities for collecting and processing data. However, such data are often obtained without a well-planned probability survey design. Such non-probability based samples cannot be automatically regarded as representative of the population of interest. Several classes of methods for estimation and inferences from non-probability samples have been developed in recent years. The quasi-randomization methods assume that non-probability sample selection is governed by an underlying latent random mechanism. The basic idea is to use information collected from a probability ("reference") sample to uncover latent non-probability survey participation probabilities (also known as "propensity scores") and use them in estimation of target finite population parameters. In this paper, we review and compare theoretical properties of recently developed methods of estimation survey participation probabilities and study their relative performances in simulations.

stat.AP↗

arXiv2023

Methods for Combining Probability and Nonprobability Samples Under Unknown Overlaps

Nonprobability (convenience) samples are increasingly sought to stabilize estimations for one or more population variables of interest that are performed 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. A recent set of approaches estimates conditional (on sampling design predictors) 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 conditional inclusion probabilities for the reference and convenience samples as our main result. Our approach allows specification of an exact likelihood for the observed sample. We construct a Bayesian hierarchical formulation that simultaneously estimates sample propensity scores and both conditional and reference sample inclusion probabilities for the convenience sample units. We compare our exact likelihood with the pseudo likelihoods in a Monte Carlo simulation study.

stat.ME↗

arXiv2023

Methods for Combining Probability and Nonprobability Samples Under Unknown Overlaps

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.

stat.ME↗

arXiv2019

On application of a response propensity model to estimation from web samples

Increasing nonresponse rates and the cost of data collection are two pressing problems encountered in traditional probability surveys. The proliferation of inexpensive data from web surveys stimulates interest in statistical techniques for valid inferences from web samples. We consider estimation of population and domain means in the two-sample setup, where the web sample contains variables of interest and covariates that are shared with an auxiliary probability survey sample. First, we propose an estimator of population mean, based on the estimated propensity of response to a web survey. This makes inferences from web samples that are similar to well-established techniques used for observational studies and missing data problems. Second, we propose an 'implicit' logistic regression for estimating parameters of the web response model in the two-sample setup. Implicit logistic regression uses selection probabilities, nominally defined for web sample units, and the size of the hypothetic population of responders to a web survey. A simulation study confirms the validity of implicit logistic regression and its higher efficiency comparing to alternative estimators of web response propensity.

stat.ME↗