Results 11 to 20 of about 845,542 (252)

Testing predictor contributions in sufficient dimension reduction

The Annals of Statistics, 2004
We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors.
Cook, R. Dennis
core   +6 more sources

Sufficient dimension reduction for compositional data. [PDF]

Biostatistics, 2021
SummaryRecent efforts to characterize the human microbiome and its relation to chronic diseases have led to a surge in statistical development for compositional data. We develop likelihood-based sufficient dimension reduction methods (SDR) to find linear combinations that contain all the information in the compositional data on an outcome variable, i.e.
Tomassi D, Forzani L, Duarte S, Pfeiffer RM.   +3 more
europepmc   +5 more sources

Tensor sufficient dimension reduction. [PDF]

Wiley Interdiscip Rev Comput Stat, 2015
Tensor is a multiway array. With the rapid development of science and technology in the past decades, large amount of tensor observations are routinely collected, processed, and stored in many scientific researches and commercial activities nowadays. The colorimetric sensor array (CSA) data is such an example.
Zhong W, Xing X, Suslick K.
europepmc   +5 more sources

Sufficient dimension reduction for censored predictors. [PDF]

Biometrics, 2017
Summary Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple correlated markers that are also censored due to lower and/or upper limits of detection, using likelihood-based sufficient dimension ...
Tomassi D, Forzani L, Bura E, Pfeiffer R.   +3 more
europepmc   +6 more sources

Projection expectile regression for sufficient dimension reduction. [PDF]

Comput Stat Data Anal, 2023
Many existing sufficient dimension reduction methods are designed for regression with predictors that are elliptically distributed, which limits their application in real data analyses. Projection expectile regression (PER) is proposed as a new linear sufficient dimension reduction method for handling complex predictor structures, which includes ...
Soale AN.
europepmc   +3 more sources

Sufficient dimension reduction for censored regressions. [PDF]

Biometrics, 2011
Methodology of sufficient dimension reduction (SDR) has offered an effective means to facilitate regression analysis of high-dimensional data. When the response is censored, however, most existing SDR estimators cannot be applied, or require some restrictive conditions.
Lu W, Li L.
europepmc   +4 more sources

EFFICIENT ESTIMATION IN SUFFICIENT DIMENSION REDUCTION. [PDF]

Ann Stat, 2013
We develop an efficient estimation procedure for identifying and estimating the central subspace. Using a new way of parameterization, we convert the problem of identifying the central subspace to the problem of estimating a finite dimensional parameter in a semiparametric model.
Ma Y, Zhu L.
europepmc   +5 more sources

Sparse kernel sufficient dimension reduction. [PDF]

J Nonparametr Stat
The sufficient dimension reduction (SDR) with sparsity has received much attention for analysing high-dimensional data. We study a nonparametric sparse kernel sufficient dimension reduction (KSDR) based on the reproducing kernel Hilbert space, which extends the methodology of the sparse SDR based on inverse moment methods.
Liu B, Xue L.
europepmc   +3 more sources

Sufficient dimension reduction via bayesian mixture modeling. [PDF]

Biometrics, 2011
Dimension reduction is central to an analysis of data with many predictors. Sufficient dimension reduction aims to identify the smallest possible number of linear combinations of the predictors, called the sufficient predictors, that retain all of the information in the predictors about the response distribution.
Reich BJ, Bondell HD, Li L.
europepmc   +5 more sources

Sufficient dimension reduction for longitudinally measured predictors. [PDF]

Stat Med, 2012
We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker ...
Pfeiffer RM, Forzani L, Bura E.
europepmc   +5 more sources