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Likelihood-Based Sufficient Dimension Reduction
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DOI：10.1198/jasa.2009.0106.png)
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Abstract
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We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.
Keywords:
Central subspace
Directional regression
Grassmann manifolds
Sliced average variance estimations
Sliced inverse regression
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