Abstract Sufficient dimension reduction (SDR) is effective in high-dimensional data analysis as it mitigates the curse of dimensionality while retaining full regression information. Missing predictors are common in high- dimensional data, yet are only discussed occasionally in the SDR context. In this paper, an inverse probability weighted sliced inverse regression (SIR) is studied with predictors missing at random. We cast SIR into the estimating equation framework to avoid inverting a large s.
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