Results 11 to 20 of about 846,250 (270)

Sufficient Dimension Reduction: An Information-Theoretic Viewpoint [PDF]

Entropy, 2022
There has been a lot of interest in sufficient dimension reduction (SDR) methodologies, as well as nonlinear extensions in the statistics literature. The SDR methodology has previously been motivated by several considerations: (a) finding data-driven ...
Debashis Ghosh
doaj   +5 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

Cumulative Median Estimation for Sufficient Dimension Reduction [PDF]

Stats, 2021
In this paper, we present the Cumulative Median Estimation (CUMed) algorithm for robust sufficient dimension reduction. Compared with non-robust competitors, this algorithm performs better when there are outliers present in the data and comparably when ...
Stephen Babos, Andreas Artemiou
doaj   +2 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

A comparative evaluation of sufficient dimension reduction and traditional statistical methods for composite biomarker score construction in diagnostic classification [PDF]

BMC Medical Research Methodology
Background Combining multiple biomarkers into a single diagnostic score can improve disease classification. However, traditional methods such as logistic regression and linear discriminant analysis depend on restrictive distributional assumptions, which ...
Hulya Ozen, Ertugrul Colak, Dogukan Ozen
doaj   +2 more sources