Results 41 to 50 of about 45,407 (261)

A Bernstein polynomial approach of the robust regression

AIMS Mathematics
This paper proposes a new family of robust non-parametric estimators for regression functions by applying polynomials to construct a robust regression estimator.
Sihem Semmar , Omar Fetitah, Mohammed Kadi Attouch, Salah Khardani, Ibrahim M. Almanjahie   +4 more
doaj   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Estimation for spatial semi-functional partial linear regression model with missing response at random

Demonstratio Mathematica
The aim of this article is to study a semi-functional partial linear regression model (SFPLR) for spatial data with responses missing at random (MAR).
Benchikh Tawfik, Almanjahie Ibrahim M., Fetitah Omar, Attouch Mohammed Kadi   +3 more
doaj   +1 more source

Hybrid Auxetic Architectures: Integrating Curvature‐Driven Design for Enhanced Mechanical Tunability and Structural Performance

Advanced Engineering Materials, EarlyView.
Curvature‐tuned auxetic lattices are designed, fabricated, and mechanically characterized to reveal how geometric curvature governs stretchability, stress redistribution, and Poisson's ratio evolution. Photoelastic experiments visualize stress pathways, while hyperelastic simulations quantify deformation mechanics.
Shuvodeep De, Giovanni Meli, Meysam Khaleghian, Anahita Emami   +3 more
wiley   +1 more source

Identification and Empirical Likelihood Inference in Nonlinear Regression Model with Nonignorable Nonresponse

Mathematics
The identification of model parameters is a central challenge in the analysis of nonignorable nonresponse data. In this paper, we propose a novel penalized semiparametric likelihood method to obtain sparse estimators for a parametric nonresponse ...
Xianwen Ding, Xiaoxia Li
doaj   +1 more source

Bias-Corrected Maximum Likelihood Estimators of the Parameters of the Unit-Weibull Distribution

Austrian Journal of Statistics, 2021
It is well known that the maximum likelihood estimates (MLEs) have appealing statistical properties. Under fairly mild conditions their asymptotic distribution is normal, and no other estimator has a smaller asymptotic variance.However, in finite samples
A. Menezes, J. Mazucheli, F. Alqallaf, M. E. Ghitany   +3 more
semanticscholar   +1 more source

Analyzing Electronic Excitations and Exciton Binding Energies in Y6 Films

Advanced Functional Materials, EarlyView.
The Y6 molecule is used for increasing the efficiency of organic solar cells. The exciton binding energy is calculated for ensembles of Y6 molecules that are representative of the typically used films. The calculations show that the excitons typically spread out over many molecules.
Sahar Javaid Akram, Sophie Meißner, Stephan Kümmel   +2 more
wiley   +1 more source

Nonconcave penalized likelihood with a diverging number of parameters

, 2003
A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed by Fan and Li to simultaneously estimate parameters and select important variables.
Fan, Jianqing, Peng, Heng
core   +4 more sources

Non‐parametric methods for doubly robust estimation of continuous treatment effects [PDF]

Journal of The Royal Statistical Society Series B-statistical Methodology, 2015
Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment.
Edward H. Kennedy, Zongming Ma, M. McHugh, Dylan S. Small   +3 more
semanticscholar   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source