Our paper proposes a method of combining probability and non-probability samples to improve analytic inference on logistic regression model parameters. A Bayesian framework is considered where only a small probability sample is available and the information from a parallel non-probability sample is provided naturally through the prior. A simulation study is run applying several informative priors. Comparisons on the performance of the models are studied with reference to their mean-squared error (MSE). In general, the informative priors reduce the MSE or, in the worst-case scenario, perform equivalently to non-informative priors.
Salvatore, C., Biffignandi, S., Sakshaug, J., Wisniowski, A., Struminskaya, B. (2022). Bayesian approach for combining probability and non-probability samples surveys.. In Book of Short Papers SIS 2022 - 51st Scientific Meeting of the Italian Statistical Society, Caserta, 22-24 giugno 2022 (pp.717-722). Milano : Pearson Italia.
Bayesian approach for combining probability and non-probability samples surveys.
Salvatore, C;
2022
Abstract
Our paper proposes a method of combining probability and non-probability samples to improve analytic inference on logistic regression model parameters. A Bayesian framework is considered where only a small probability sample is available and the information from a parallel non-probability sample is provided naturally through the prior. A simulation study is run applying several informative priors. Comparisons on the performance of the models are studied with reference to their mean-squared error (MSE). In general, the informative priors reduce the MSE or, in the worst-case scenario, perform equivalently to non-informative priors.
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