Results 131 to 140 of about 1,312,626 (282)

Variable selection for a mark-specific additive hazards model using the adaptive LASSO. [PDF]

Stat Methods Med Res, 2021
Han D, Qu L, Sun L, Sun Y.
europepmc   +1 more source

Effective Performance of the 2022 American College of Rheumatology/EULAR Classification Criteria for Antineutrophil Cytoplasmic Antibody–Associated Vasculitis in Pediatric Patients: An ARChiVe Study

Arthritis &Rheumatology, EarlyView.
Objective To assess the 2022 American College of Rheumatology (ACR)/EULAR classification criteria for antineutrophil cytoplasmic antibody–associated vasculitis (AAV) in children with chronic small‐to‐medium vessel vasculitis. Methods A cohort of 574 patients, identified by physician's diagnosis (MD‐diagnosis) in A Registry of Childhood Vasculitis, was ...
David A. Cabral, Else S. Bosman, Nick McPhate, Simranpreet K. Mann, Kirandeep K. Toor, Kimberly A. Morishita, Raashid Luqmani, Michael W. Beresford, James Bistolarides, Sarah Campillo, Sirirat Charuvanij, Kathryn Cook, Paul Dancey, Marietta de Guzman, Samundeeswari Deepak, Barbara Eberhard, Melissa Elder, Samuel Gagne, Katie Harrison, Adam Huber, Archana Khan, Susan Kim, Marisa S. Klein‐Gitelman, Tzielan C. Lee, Suzanne C. Li, Neil Martin, Flora McErlane, L. Nandini Moorthy, Alvaro H. Orjuela, Jonathan Park, Phil Riley, Alan M. Rosenberg, Susan Shenoi, Vidya Sivaraman, Sodality Sutnga, Tamara Tanner, Stacey E. Tarvin, Marinka Twilt, Linda Wagner‐Weiner, Rae S. M. Yeung, Kelly L. Brown, for the ARChiVe Investigators Network within the PedVas Initiative   +41 more
wiley   +1 more source

Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO. [PDF]

J Am Stat Assoc, 2021
Zhang C, Dinh VU, Matsen FA.
europepmc   +1 more source

Hierarchical Bayesian adaptive lasso methods on exponential random graph models

Applied Network Science
The analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One
Dan Han, Vicki Modisette, Melinda Forthofer, Rajib Paul   +3 more
doaj   +1 more source

A Modified Adaptive Sparse-Group LASSO Regularization for Optimal Portfolio Selection

IEEE Access
Mean-variance portfolio optimization is widely used by financial professionals as a fundamental strategy for constructing portfolios that achieve the highest returns for a given degree of risk tolerance.
Somaya Sadik, Mohamed Et-Tolba, Benayad Nsiri   +2 more
doaj   +1 more source

The oracle property of the generalized outcome-adaptive lasso

Statistics & Probability Letters
The generalized outcome-adaptive lasso (GOAL) is a variable selection for high-dimensional causal inference proposed by Baldé et al. [2023, {\em Biometrics} {\bfseries 79(1)}, 514--520]. When the dimension is high, it is now well established that an ideal variable selection method should have the oracle property to ensure the optimal large sample ...
openaire   +3 more sources

Comparative Transcriptional Profiling of Key Macrophage and Fibroblast Subpopulations in Rheumatoid Arthritis–Associated Lung Disease

Arthritis &Rheumatology, EarlyView.
Objective Rheumatoid arthritis (RA) often involves extra‐articular complications, including interstitial lung disease (ILD) and/or pulmonary nodules. Transcriptomic profiling of lung tissue provides the opportunity to directly assess cell‐specific gene expression and corresponding pathway activation in different types of rheumatoid lung disease ...
Tracy Tabib, Ogechukwu Ezenwa, Joshua Sciurba, Eleanor B. Valenzi, John Sembrat, Brittany Cody, Jishnu Das, Robert Lafyatis, Dana P. Ascherman   +8 more
wiley   +1 more source

Adaptive Group LASSO for the GARCH-MIDAS Model

Journal of Business & Economic Statistics
This article investigates the variable selection problem in the generalized autoregressive conditional heteroscedasticity-mixed-data sampling (GARCH-MIDAS) model of Engle, Ghysels, and Sohn (2013), and establishes the corresponding theoretical properties along with a feasible computational algorithm.
Pan, Zhiyuan, Wang, Yudong, Huang, Juan, Zhang, Yaojie   +3 more
openaire   +1 more source

Performance improvement of discrete‐time linear‐quadratic regulators applied to uncertain linear systems using the Tikhonov regularization method

Asian Journal of Control, EarlyView.
Abstract The linear‐quadratic regulator (LQR) problem of optimal control of an uncertain discrete‐time linear system (DTLS) is revisited in this paper from the perspective of Tikhonov regularization. We show that an optimally chosen regularization parameter reduces, compared to the classical LQR, the values of a scalar error function, as well as the ...
Fernando Pazos, Amit Bhaya
wiley   +1 more source

LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) DAN ADAPTIVE RIDGE (Least Absolute Shrinkage and Selection Operator (LASSO) and Adaptive Ridge)

, 2014
The LASSO is a shrinkage and selection method for linear regression. It minimizes sum square of residual subject to sum of absolute coefficient less than a constant.
Yuliani Setia Dewi
core