Results 1 to 10 of about 10,491 (149)

Central Limit Theorem in View of Subspace Convex-Cyclic Operators [PDF]

Қарағанды университетінің хабаршысы. Математика сериясы, 2021
In our work we have defined an operator called subspace convex-cyclic operator. The property of this newly defined operator relates eigenvalues which have eigenvectors of modulus one with kernels of the operator.
H.M. Hasan, D.F. Ahmed, M.F. Hama, K.H.F. Jwamer   +3 more
doaj   +3 more sources

Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Regression [PDF]

Journal of the American Statistical Association, 2006
In regression with a high-dimensional predictor vector, it is important to estimate the central and central mean subspaces that preserve sufficient information about the response and the mean response. Using the Fourier transform, we have derived the candidate matrices whose column spaces recover the central and central mean subspaces exhaustively ...
Peng Zeng
exaly   +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

Learning Functions Varying along a Central Subspace

SIAM Journal on Mathematics of Data Science
Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of a $s$-Hölder function $f$ in $\mathbb{R}^D$ which varies along a central subspace of dimension $d$ while $d\ll D$. A direct approximation of $f$ in $\mathbb{R}^D$ with an $\varepsilon$ accuracy requires the number of ...
, Wenjing Liao
exaly   +4 more sources

Optimal transformation: A new approach for covering the central subspace

Journal of Multivariate Analysis, 2013
This paper studies a general family of methods for sufficient dimension reduction (SDR) called the test function (TF), based on the introduction of a nonlinear transformation of the response. By considering order 1 and 2 conditional moments of the predictors given the response, we distinguish two classes of methods.
Bernard Delyon
exaly   +4 more sources

Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products

SIAM Journal on Mathematics of Data Science
We consider the problem of sufficient dimension reduction (SDR) for multi-index models. The estimators of the central mean subspace in prior works either have slow (non-parametric) convergence rates, or rely on stringent distributional conditions (e.g., the covariate distribution $P_{\mathbf{X}}$ being elliptical symmetric). In this paper, we show that
Daniel Hsu, Gan Yuan
exaly   +4 more sources

DISA tool: Discriminative and informative subspace assessment with categorical and numerical outcomes

PLoS ONE, 2022
Pattern discovery and subspace clustering play a central role in the biological domain, supporting for instance putative regulatory module discovery from omics data for both descriptive and predictive ends.
Leonardo Alexandre, Rafael S. Costa, Rui Henriques   +2 more
doaj   +2 more sources

Sufficient Dimension Reduction: An Information-Theoretic Viewpoint

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   +1 more source

Transformed central quantile subspace [PDF]

Statistics, 2021
arXiv admin note: text overlap with arXiv:1906 ...
openaire   +2 more sources

Graph adaptive semi-supervised discriminative subspace learning for EEG emotion recognition

Journal of King Saud University: Computer and Information Sciences, 2023
Since Electroencephalogram (EEG) is resistant to camouflage and contains abundant neurophysiological information, it shows significant superiorities in objective emotion recognition, making EEG-based emotion recognition become a hot research field in ...
Fengzhe Jin, Yong Peng, Feiwei Qin, Junhua Li, Wanzeng Kong   +4 more
doaj   +1 more source