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• W3105229437 abstract "Correlated outcomes are common in many practical problems. Based on a decomposition of estimation bias into two types, within-subspace and against-subspace, we develop a robust approach to estimating the classification rule for the outcome of interest with the presence of auxiliary outcomes in high-dimensional settings. The proposed method includes a pooled estimation step using all outcomes to gain efficiency, and a subsequent calibration step using only the outcome of interest to correct both types of biases. We show that when the pooled estimator has a low estimation error and a sparse against-subspace bias, the calibrated estimator can achieve a lower estimation error than that when using only the single outcome of interest. An inference procedure for the calibrated estimator is also provided. Simulations and a real data analysis are conducted to justify the superiority of the proposed method." @default.
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• W3105229437 date "2020-11-11" @default.
• W3105229437 modified "2023-09-27" @default.
• W3105229437 title "Learning a high-dimensional classification rule using auxiliary outcomes" @default.
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