Localized Sliced Inverse Regression

A note on shrinkage sliced inverse regression

We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage sliced inverse regression estimator, which provides easier interpretations and better prediction accuracy without assuming a parametric model. The shrinkage sliced inverse regression approach can be employed for both single-index and multiple-index models. Simulation studies suggest that the new...

An investigation of sliced inverse regression with censored data

An Investigation of Sliced Inverse Regression with Censored Data Daniel Riggs August,62010 The complexity of high-dimensional data creates a number of concerns when trying to analyze it. This data often consists of a response or survival time and potentially thousands of predictors. These predictors can be highly correlated, and the sample size is often very small and right censored. Sliced inv...

Sliced Inverse Regression for Dimension Reduction

Sufficient dimension reduction in regressions across heterogeneous subpopulations

Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this method to regressions with qualitative predictors and developed a method, partial sliced inverse regression, under the assumption that the covariance matrices of the continuous predictors are constant across the levels of the qualitative predictor. We extend partial sliced i...

Cluster-based regularized sliced inverse regression for forecasting macroeconomic variables

This article concerns the dimension reduction in regression for large dataset. We introduce a new method based on the sliced inverse regression approach, called cluster-based regularized sliced inverse regression. Our method not only keeps the merit of considering both response and predictors information, but also enhances the capability of handling highly correlated variables. It is justified ...