Results 51 to 60 of about 4,185 (251)

Statistical Inference for High-Dimensional Heteroscedastic Partially Single-Index Models

Entropy
In this study, we propose a novel penalized empirical likelihood approach that simultaneously performs parameter estimation and variable selection in heteroscedastic partially linear single-index models with a diverging number of parameters.
Jianglin Fang, Zhikun Tian
doaj   +1 more source

Robust Adaptive Lasso method for parameter's estimation and variable selection in high-dimensional sparse models. [PDF]

PLoS ONE, 2017
High dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. To address this issue different penalized regression procedures have been introduced in the litrature, but these methods
Abdul Wahid, Dost Muhammad Khan, Ijaz Hussain   +2 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Poisson Mixed-Effects Count Regression Model Based on Double SCAD Penalty and Its Simulation Study

Axioms
This paper focuses on variable selection and parameter estimation for mixed-effects Poisson count regression models. To simultaneously select important variables in both fixed effects and random effects, we propose a double-penalized Poisson count ...
Keqian Li, Xueni Ren, Hanfang Li, Youxi Luo   +3 more
doaj   +1 more source

Value of Information of Improved Traceability in Fresh Produce Markets

Agribusiness, EarlyView.
ABSTRACT Traceability plays an important role in promoting a safe food supply by fostering transparent information exchange along the food supply chain. New technological innovations have the potential to improve traceability outcomes, as greater transparency along the food supply chain can aid in pinpointing precise origins of the contamination ...
Kelsey Vourazeris, Mark R. Manfredo, Michael N. Kozicki   +2 more
wiley   +1 more source

Bias-corrected inference for multivariate nonparametric regression: Model selection and oracle property [PDF]

Journal of Multivariate Analysis, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
GIORDANO, Francesco, PARRELLA, Maria Lucia   +1 more
openaire   +4 more sources

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

LLM-Based Test Oracles: Source-of-Authority Taxonomy—A Systematic Literature Review

IEEE Access
Large language models (LLMs) increasingly decide whether software behaves correctly, either by writing a test oracle or by acting as one. Yet two oracles can look identical and rest on different ground: one assertion encodes a written specification ...
Ali Hassaan Mughal, Muhammad Bilal
doaj   +1 more source

Interpretable Short‐Term Electric Load Forecasting

Advanced Intelligent Systems, EarlyView.
A temporal fusion transformer is implemented to generate day‐ahead forecasts of the hourly electrical load of a departmentbuilding at an Italian university. A forecasting performance improvement of more than 25% compared with established benchmark models and a provision of inherent robust interpretability insights reveal the potential of this model for
Alessandro Nicola, Giorgia Ghione, Vincenzo Randazzo, Vincenzo Franzitta, Marco Badami, Eros Pasero, Giansalvo Cirrincione   +6 more
wiley   +1 more source

Preferential trading in agriculture: New insights from a structural gravity analysis and machine learning

American Journal of Agricultural Economics, EarlyView.
Abstract Preferential trade agreements (PTAs) contain various non‐tariff provisions, yet identifying their trade effects remains challenging because these commitments are high‐dimensional and strongly correlated within agreements. We estimated a theory‐consistent structural gravity model with domestic flows for 26 agricultural subsectors over 1988–2017
Dongin Kim, Sandro Steinbach
wiley   +1 more source