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Most business processes are, by nature, multivariate and autocorrelated. Highdimensionality is rooted in processes where more than one variable is considered simultaneously to provide a more comprehen...
We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on...
We analyse the properties of the Principal Fitted Components (PFC) algorithm proposed by Cook. We derive theoretical properties of the resulting estimators, including sufficient conditions under which...
Suppose the data consist of a set S of points xj , 1 ≤ j ≤ J, distributed in a bounded domain D RN, where N is a large number. An algorithm is given for finding the sets Lk of dimension k N, k = 1, ...
The “curse of dimensionality” has remained a challenge for highdimensional data analysis in statistics. The sliced inverse regression (SIR) and canonical correlation (CANCOR) methods aim to reduce ...
The statistical problem of estimating the effective dimensionreduction (EDR) subspace in the multi-index regression model with deterministic design and additive noise is considered. A new procedure ...
In this paper, we propose two new methods to estimate the dimensionreduction directions of the central subspace (CS) by constructing a regression model such that the directions are all captured in t...

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