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A Semiparametric Model Approach to Data Integration under Missing Not at Random
Integrating probability and non-probability samples requires accounting for an unknown selection mechanism that may depend on the outcome. We develop a semiparametric approach in which an unspecified function captures covariate dependence and a scalar tilting parameter governs outcome dependence. Id
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