19 May 2017 The second approach is sufficient dimension reduction, which seeks a few linear A multiple-index model and dimension reduction. semiparametric/non-parametric estimation. Specifically, one can recast the dimension reduction model (2) into an equivalent but more familiar multiple-index   MILFM: Multiple index latent factor model based on high‐dimensional features Sliced Inverse Regression for Dimension Reduction (With Discussion). Article. A particularly relevant model for which (IR1) holds is the multiple-index model. Y = ℓ(ξ1 inverse regression function E(Xt|Y ) that facilitates dimension reduction.

We then apply this inequality to the adaptive estimation of a multivariate density in a “multiple index” model. We show that the proposed aggregate estimator 

A new dimension-reduction method involving slicing the region of the response and applying local kernel regression to each slice is proposed. Compared with the traditional inverse regression methods [e.g., sliced inverse regression (SIR)], the new method is free of the linearity condition and has much better estimation accuracy. This paper concerns robust and efficient direction identification for a groupwise additive multiple-index model, in which each additive function has a single-index structure. Interestingly, without involving non-parametric approach, we show that the directions of all the index parameter vectors can be recovered by a simple linear composite quantile regression (CQR). As a specific application

index models, founded on a sequence of linear regressions. Here we borrow ideas from dimension reduction in models that involve high-dimensional, but.

22 Aug 2016 Motivated from problems in canonical correlation analysis, reduced rank regression and sufficient dimension reduction, we introduce a double  The span is referred to as the effective dimension reduction space (EDR space). SIR tries to find this $ k$ -dimensional subspace of $ \mathbb{R}^p$ by  In this paper, a model-adaptation concept in lack-of-fit testing is introduced and a dimension-reduction model- adaptive test procedure is proposed for parametric  13 Feb 2002 A regression-type model for dimension reduction can be written as y = g. least squares estimation of multiple index models: single equation. We then apply this inequality to the adaptive estimation of a multivariate density in a “multiple index” model. We show that the proposed aggregate estimator  6 Aug 2010 In this article we propose a Bayesian sufficient dimension reduction To extend the single-index model to the multiple-index model with d > 1, 

MILFM: Multiple index latent factor model based on high‐dimensional features Sliced Inverse Regression for Dimension Reduction (With Discussion). Article.

semiparametric/non-parametric estimation. Specifically, one can recast the dimension reduction model (2) into an equivalent but more familiar multiple-index   MILFM: Multiple index latent factor model based on high‐dimensional features Sliced Inverse Regression for Dimension Reduction (With Discussion). Article. A particularly relevant model for which (IR1) holds is the multiple-index model. Y = ℓ(ξ1 inverse regression function E(Xt|Y ) that facilitates dimension reduction. 20 May 2015 The proposed method extends the sufficient dimension reduction to We also show that the natural method of running multiple regression of target on approximate factor model, principal components, learning indices, 

A multiple-index model and dimension reduction By Y. Xia Topics: Asymptotic distribution, Convergence of algorithm, Dimension reduction, Local linear smoother, Semiparametric model.

In every image, there are high number of pixels i.e. high number of dimensions. And every dimension is important here. You can’t omit dimensions randomly to make better sense of your overall data set. In such cases, dimension reduction techniques help you to find the significant dimension(s) using various method(s). the estimation of high-dimensional functions. This difficulty has led to an emphasis on the so-called functional linear model, which is much more flexible than common linear models in finite dimension, but nevertheless imposes structural constraints on the relationship be-tween predictors and responses. Recent advances have extended the

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