<p>Within the sufficient dimension reduction framework, research on nonignorable missing data remains relatively scarce, primarily due to the associated identifiability issues. This paper considers the problem of sufficient dimension reduction when the response is subject to nonignorable missingness. By adopting a flexible semiparametric missingness mechanism to ensure identifiability, the authors construct three classes of estimating equations based on inverse probability weighting, regression imputation and augmented inverse probability weighting. The novel aspects of the proposed methods also include the incorporation of sufficient dimension reduction techniques in the implementation of these estimating equations to mitigate the high-dimensional effect, and the construction of the estimator for the conditional expectation of the estimating functions given both the covariates and the missingness indicator. The authors prove that the resulting three estimators are asymptotically normally distributed. Comprehensive simulation studies are conducted to assess the finite-sample performance of the proposed methods, and an application to PM2.5 concentration data is also presented.</p>