Results 131 to 140 of about 2,638,357 (294)
A computer intensive method for choosing the ridge parameter [PDF]
In this paper we describe a computer intensive method to find the ridge parameter in a prediction oriented linear model. With the help of a factorial experimental design the method is tested and compared to a classical one.
Weihs, Claus +2 more
core
Meningeal Lymphatic LTBR Deficiency Links Spinal Cord Injury to Secondary Brain Pathology
Advanced Science, EarlyView.Spinal cord injury (SCI) induces remote brain alterations beyond the injured spinal cord. This study links dysfunction of meningeal lymphatic vessels (mLVs) to neuroinflammation and neuronal injury after SCI. LTBR‐mediated lymphatic restoration improves meningeal lymphatic integrity and neurological outcomes, highlighting LTBR as a potential regulator ...
Yi Chen +12 more
wiley +1 more source
The multivariate least trimmed squares estimator. [PDF]
In this paper we introduce the least trimmed squares estimator for multivariate regression. We give three equivalent formulations of the estimator and obtain its breakdown point. A fast algorithm for its computation is proposed.
Croux, Christophe +2 more
core
Ridge-Type MML Estimator in the Linear Regression Model
, 2019 WOS: 000461317600026Ridge regression is widely used to deal with the multicollinearity problem. However, traditional ridge estimator (Hoerl and Kennard 1970) loses its efficiency in the presence of outliers, since it is obtained based on least squares ...
Şenoğlu, Birdal, Acıtaş, Şükrü
core +1 more source
Advanced Science, EarlyView.This study established a one‐step method inducing pro‐spermatogonia‐like (activating SOX17) and oogonia‐like (activating SOX17/TFAP2C) cells from porcine pgEpiSCs with a NANOS3‐tdTomato reporter. EGF activates the core PGC program and supports survival; BMP4 further induces NANOS3 and DDX4 expression; IWR1 enhances PGCLC efficiency by suppressing ...
Pengcheng He +14 more
wiley +1 more source
Bayesian Analyses of Ridge Regression Prooblems
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2017 A Bayesian formulation of the ridge regression problem is considerd, which derives from a direct specification of prior informations about parameters of general linear regression model when data suffer from a high degree of multicollinearity.A new ...
H. M. Gorgees
doaj
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
Advanced Electronic Materials, EarlyView.ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels +4 more
wiley +1 more source
Advanced Electronic Materials, EarlyView.A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun +5 more
wiley +1 more source
A stochastic restricted ridge regression estimator
Groß [J. Groß, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57-64] proposed a restricted ridge regression estimator when exact restrictions are assumed to hold.
Özkale, M. Revan
core
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source