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Optimal quantization applied to sliced inverse regression [PDF]
Journal of Statistical Planning and Inference, 2012 In this paper we consider a semiparametric regression model involving a $d$-dimensional quantitative explanatory variable $X$ and including a dimension reduction of $X$ via an index $ 'X$. In this model, the main goal is to estimate the euclidean parameter $ $ and to predict the real response variable $Y$ conditionally to $X$.
Gégout-Petit, Anne +2 more
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Sparse sufficient dimension reduction for directional regression
Journal of Big DataSufficient dimension reduction has emerged as a powerful tool for extracting meaningful information within high dimensional datasets over the past few decades.
Gayun Kwon, Gijeong Noh, Kyongwon Kim
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Online Kernel Sliced Inverse Regression
, 2023 Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in depth, but as far as we know, online supervised nonlinear dimension reduction methods have not been fully ...
Cui, Wenquan +3 more
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Tobit Model Estimation and Sliced Inverse Regression [PDF]
, 2006 It is not unusual for the response variable in a regression model to be subject to censoring or truncation. Tobit regression models are a specific example of such a situation, where for some observations the observed response is not the actual response ...
Li, Lexin +2 more
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Sliced Average Variance Estimation for Multivariate Time Series
, 2018 Supervised dimension reduction for time series is challenging as there may be temporal dependence between the response $y$ and the predictors $\boldsymbol x$. Recently a time series version of sliced inverse regression, TSIR, was suggested, which applies
Croux, Christophe +3 more
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Functional Lagged Regression with Sparse Noisy Observations
, 2020 A functional (lagged) time series regression model involves the regression of scalar response time series on a time series of regressors that consists of a sequence of random functions.
Panaretos, Victor M., Rubín, Tomáš
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Sliced Inverse Regression in Metric Spaces
Statistica SinicaIn this article, we propose a general nonlinear sufficient dimension reduction (SDR) framework when both the predictor and response lie in some general metric spaces. We construct reproducing kernel Hilbert spaces whose kernels are fully determined by the distance functions of the metric spaces, then leverage the inherent structures of these spaces to ...
Virta, Joni, Lee, Kuang-Yao, Li, Lexin
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Graph imposed sliced inverse regression [PDF]
, 2021 A new method is developed for performing sufficient dimension reduction when probabilistic graphical models are being used to perform estimation of parameters. The procedure enriches the domain of application of dimension reduction techniques to settings where (i) p the number of variables in the model is much larger than the available sample size n ...
Pircalabelu, Eugen, Artemiou, Andreas
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Sliced inverse regression in reference curves estimation [PDF]
Computational Statistics & Data Analysis, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gannoun, Ali +3 more
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Sliced Inverse Regression for High-dimensional Time Series [PDF]
, 2001 Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags.
Becker, Claudia, Fried, Roland
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