Structured time‐dependent inverse regression (STIR)
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DOI: 10.1002/sim.9670
Abstract: We propose and study structured time‐dependent inverse regression (STIR), a novel sufficient dimension reduction model, to analyze longitudinally measured, correlated biomarkers in relation to an outcome. The time structure is accommodated in an inverse regression… read more here.
Keywords: time; regression; time dependent; structured time ... See more keywords
Robust functional sliced inverse regression
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DOI: 10.1007/s00362-015-0695-x
Abstract: Functional data are infinite-dimensional statistical objects which pose significant challenges to both theorists and practitioners. To avoid the stringent constraints for parametric methods and low convergence rate for nonparametric methods, many functional dimension reduction methods… read more here.
Keywords: regression; robust functional; functional dimension; sliced inverse ... See more keywords
Multiple-population shrinkage estimation via sliced inverse regression
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DOI: 10.1007/s11222-015-9609-y
Abstract: The problem of dimension reduction in multiple regressions is investigated in this paper, in which data are from several populations that share the same variables. Assuming that the set of relevant predictors is the same… read more here.
Keywords: estimation; regression; sliced inverse; shrinkage ... See more keywords
Direct instrumental nonparametric estimation of inverse regression functions
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DOI: 10.1016/j.jeconom.2017.07.001
Abstract: This paper treats the estimation of the inverse g−1 of a monotonic function g satisfying E[Y−g(X)|W]=0 where (X,W) is continuously distributed. Using instrumental restrictions, many parameters of interest in econometrics can be expressed as inverses… read more here.
Keywords: estimation inverse; inverse regression; direct instrumental; nonparametric estimation ... See more keywords
On expectile-assisted inverse regression estimation for sufficient dimension reduction
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DOI: 10.1016/j.jspi.2020.11.004
Abstract: Moment-based sufficient dimension reduction methods such as sliced inverse regression may not work well in the presence of heteroscedasticity. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimension… read more here.
Keywords: inverse regression; dimension reduction; regression; sufficient dimension ... See more keywords
Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction
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DOI: 10.5705/ss.202022.0112
Abstract: Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and methods of high-dimensional SIR, two remaining challenges… read more here.
Keywords: high dimensional; inverse regression; dimension reduction; sufficient dimension ... See more keywords