We propose a real-time approach for sufficient dimension reduction. Compared with popular reduction methods including sliced inverse regression and principal support vector machines, the proposed least squares machines enjoys better estimation of central subspace. Furthermore, this new proposal can be used in presence streamed data quick updates. It is demonstrated through simulations real appl...

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel es...

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel es...

Addressing covariate imbalance in causal analysis will be reformulated as an elimination of the nuisance variables problem. We show, within a counterfactual balanced setting, how averaging, conditioning, and marginalization techniques can be used to reduce bias due to a possibly large number of imbalanced baseline confounders. The notions of X-sufficient and X-ancillary quantities are discussed...

Abstract It has previously been shown that ordinary least squares can be used to estimate the coefficients of single-index model under only mild conditions. However, estimator is non-robust leading poor estimates for some models. In this paper we propose a new sliced least-squares utilizes ideas from Sliced Inverse Regression. Slices with problematic observations contribute high variability in ...

In this study, we propose an integrated approach based on iterative sliced inverse regression (ISIR) for the segmentation of ultrasound and magnetic resonance (MR) images. The approach integrates two stages. The first is the unsupervised clustering which combines multidimensional scaling (MDS) with K-Means. The dimension reduction based on MDS is employed to obtain fewer representative variates...