Results 41 to 50 of about 2,762 (131)
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm
, 2015 The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing.
Lin, Zhouchen +3 more
core +1 more source
Simultaneous Feature Selection for Optimal Dynamic Treatment Regimens
Statistics in Medicine, Volume 44, Issue 15-17, July 2025.ABSTRACT Dynamic treatment regimens (DTRs), where treatment decisions are tailored to individual patient's characteristics and evolving health status over multiple stages, have gained increasing interest in the modern era of precision medicine. Identifying important features that drive these decisions over stages not only leads to parsimonious DTRs for
Mochuan Liu, Yuanjia Wang, Donglin Zeng
wiley +1 more source
Variable selection in semiparametric regression modeling
, 2008 In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of ...
Li, Runze, Liang, Hua
core +2 more sources
Bendable Fiber Lens for Minimally Invasive Endoscopy
Laser &Photonics Reviews, Volume 19, Issue 12, June 18, 2025.Translating ex vivo imaging to minimally invasive in vivo applications is of outstanding scientific and socioeconomic relevance. 3D imaging based on tiny lensless fiber endoscopes requires complex setups and computational schemes, so far. Bridging this gap, imaging through a unique optical fiber that mimics the functionality of a lens but is minimally ...
Ronja Stephan +10 more
wiley +1 more source
High‐Dimensional Multiresponse Partially Functional Linear Regression
Statistics in Medicine, Volume 44, Issue 13-14, June 2025.ABSTRACT We propose a new class of high‐dimensional multiresponse partially functional linear regressions (MR‐PFLRs) to investigate the relationship between scalar responses and a set of explanatory variables, which include both functional and scalar types.
Xiong Cai +3 more
wiley +1 more source
On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series
IEEE Open Journal of Signal ProcessingEstimation of the conditional independence graph (CIG) of high-dimensional multivariate Gaussian time series from multi-attribute data is considered.
Jitendra K. Tugnait
doaj +1 more source
Likelihood Adaptively Modified Penalties [PDF]
, 2013 A new family of penalty functions, adaptive to likelihood, is introduced for model selection in general regression models. It arises naturally through assuming certain types of prior distribution on the regression parameters.
Feng, Yang, Li, Tengfei, Ying, Zhiliang
core
, 2012 Modern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry, and electroencephalography, matrix type covariates frequently arise when measurements are obtained for each ...
Li, Lexin, Zhou, Hua
core +1 more source
Joint Robust Variable Selection of Mean and Covariance Model via Shrinkage Methods
International Statistical Review, Volume 93, Issue 1, Page 102-129, April 2025.Summary A valuable and robust extension of the traditional joint mean and the covariance models when data subject to outliers and/or heavy‐tailed outcomes can be achieved using the joint modelling of location and scatter matrix of the multivariate t‐distribution. This model encompasses three models in itself, and the number of unknown parameters in the
Yeşim Güney +2 more
wiley +1 more source
No penalty no tears: Least squares in high-dimensional linear models [PDF]
, 2016 Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size.
Dunson, David +2 more
core +1 more source