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Bayesian Model Averaging Sufficient Dimension Reduction
[article]
2020
In sufficient dimension reduction (Li, 1991; Cook, 1998b), original predictors are replaced by their low-dimensional linear combinations while preserving all of the conditional information of the response given the predictors. Sliced inverse regression [SIR; Li, 1991] and principal Hessian directions [PHD; Li, 1992] are two popular sufficient dimension reduction methods, and both SIR and PHD estimators involve all of the original predictor variables. To deal with the cases when the linear
doi:10.34944/dspace/3403
fatcat:oqt75ttznbd5tlpercdf33avoy