Results 101 to 110 of about 71,763 (309)
Austrian Journal of Statistics, 2016 N. BALAKRISHNAN, V.B. MELAS, S. ERMAKOV (Editors) Advances in Stochastic Simulation Methods. Statistics for Industry and Technology. Boston: Birkhäuser 2000, XXVI+ 386 S., ISBN 0-8176-4107-6. Yadolah DODGE, Jana JURE?KOVA: Adaptive Regression.
E. Stadlober +2 more
doaj +1 more source
Advanced Science, EarlyView.Metastasis remains a major challenge in colorectal cancer. Using an in vivo shRNA screening system, this study identifies Homeobox D4 as a key metastasis suppressor. Reduced Homeobox D4 expression is associated with aggressive tumor features. Functional and mechanistic analyses show that it inhibits epithelial‐mesenchymal transition by repressing ...
Zhi‐hua Ye +9 more
wiley +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
Advanced Science, EarlyView.This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
Model Specification Tests in Nonparametric Stochastic Regression Models [PDF]
In this paper, we consider testing for additivity in a class of nonparametric stochastic regression models. Two test statistics are constructed and their asymptotic distributions are established.
Gao, Jiti, Wolff, Rodney, Tong, Howell
core
Targeting Supramolecular Active Complexes of Nav1.7/Nav1.8 to Relieve Chronic Neuropathic Pain
Advanced Science, EarlyView.In mice and patients with severe chronic neuropathic pain (NP), Nav1.7, Nav1.8, TrkB, and five cytoskeletal proteins form supramolecular active complexes (SMACs) with polygonal lattice structures as noxious signal amplifiers in dorsal root ganglion (DRG) neurons.
Liting Sun +27 more
wiley +1 more source
Asymptotic properties of projections with applications to stochastic regression problems [PDF]
Almost sure convergence properties of least-squares estimates in stochastic regression models and an asymptotic theory of related Euclidean projections are developed herein. Applications to autoregressive processes and to dynamic input-output systems are
Lai, T. L., Wei, C. Z.
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Nonparametric estimation of ratios of noise to signal in stochastic regression [PDF]
, 2000 In this paper, we study three different types of estimates for the noise-to signal ratios in a general stochastic regression setup. The locally linear and locally quadratic regression estimators serve as the building blocks in our approach.
Yao, Qiwei, Tong, Howell
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Residual empirical processes for long and short memory time series [PDF]
, 2008 This paper studies the residual empirical process of long- and short-memory time series regression models and establishes its uniform expansion under a general framework.
Ngai, Hang Chan, Ling, Shiqing
core +1 more source
Advanced Science, EarlyView.Whole‐genome analysis of 1,054 chickens reveals three ancestral sources (NWC, SYA, and SHF) with distinct temporal entry patterns into the Tibetan Plateau. Route‐specific selection scans, calibrated against a demographic null, suggest complementary functional enrichments—vascular homeostasis (NWC), calcium signaling and cardiac adaptation (SYA), and ...
Zongyi Zhao +7 more
wiley +1 more source
Strong Consistency of Bayes Estimates in Stochastic Regression Models [PDF]
Under minimum assumptions on the stochastic regressors, strong consistency of Bayes estimates is established in stochastic regression models in two cases: (1)Â When the prior distribution is discrete, the p.d.f.fof i.i.d. random errors is assumed to have
Hu, Inchi
core