Sliced inverse regression (SIR) is an important method for reducing the dimensionality of input variables. Its goal is to estimate the effective dimension reduction directions. In classification settings, SIR is closely related to Fisher discriminant analysis. Motivated by reproducing kernel theory, we propose a notion of nonlinear effective dimension reduction and develop a nonlinear extension...

the aim of this paper is to extend a hybrid protection plan for power transformer (pt) based on mra-ksir-ssvm. this paper offers a new scheme for protection of power transformers to distinguish internal faults from inrush currents. some significant characteristics of differential currents in the real pt operating circumstances are extracted. in this paper, multi resolution analysis (mra) is use...

The aim of this paper is to extend a hybrid protection plan for Power Transformer (PT) based on MRA-KSIR-SSVM. This paper offers a new scheme for protection of power transformers to distinguish internal faults from inrush currents. Some significant characteristics of differential currents in the real PT operating circumstances are extracted. In this paper, Multi Resolution Analysis (MRA) is use...

We develop an extension of the sliced inverse regression (SIR) framework for dimension reduction using kernel models and Tikhonov regularization. The result is a numerically stable nonlinear dimension reduction method. We prove consistency of the method under weak conditions even when the reproducing kernel Hilbert space induced by the kernel is infinite dimensional. We illustrate the utility o...

Most of the methods among the inverse regression literature rely on a slicing of the range of the response variable. Theoretical results are usually shown assuming that (i) the slices are fixed while in practice estimators are constructed with (ii) random slices that contain the same number of observations. In this paper we obtain the asymptotic normality in the case where the slices contains t...