Results 121 to 130 of about 56,202 (281)

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

Fast Marginal Likelihood Estimation of the Ridge Parameter(s) in Ridge Regression and Generalized Ridge Regression for Big Data

, 2014
Unlike the ordinary least-squares (OLS) estimator for the linear model, a ridge regression linear model provides coefficient estimates via shrinkage, usually with improved mean-square and prediction error. This is true especially when the observed design matrix is ill-conditioned or singular, either as a result of highly-correlated covariates or the ...
openaire   +2 more sources

Digital Technologies Disclosure and the Cost of Capital: The Mediating Role of Sustainability Performance

Business Strategy and the Environment, EarlyView.
ABSTRACT This study examines the economic consequences of Digital Technologies Disclosure (DTD), focusing on its impact on the cost of capital. The increasing significance of digital transformation in shaping corporate strategies and market perceptions motivates the study.
Hussein Mohsen Saber Ahmed, Awad Elsayed Awad Ibrahim, Khaldoon Albitar   +2 more
wiley   +1 more source

Patterns and Drivers of Spanish Corporate Commitment to the UN Global Compact: A Quantitative Approach

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This paper presents a quantitative assessment of Spanish companies' commitment to the United Nations Global Compact (UNGC) and the Sustainable Development Goals (SDGs). Analyzing over 1000 participating firms, we identify prioritization patterns and examine structural factors influencing SDG adherence.
Juan Laborda, Juan Pérez
wiley   +1 more source

AI‐Driven Precision Annealing for High Performance Fe‐Based Amorphous Alloys

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
The four stages of the research process are as follows: First, data is collected and a database is constructed. This is followed by feature selection and analysis, then the establishment of machine learning models, and finally formulation design and preparation.
Yichuan Tang, Shaopeng Liu, Silong Li, Ruonan Ma, Peinan Li, Zheng Wang, Kun Wang, Kaiyan Cao, Sidan Ding, Chao Zhou, Fanghua Tian, Sen Yang, Minxia Fang, Yin Zhang   +13 more
wiley   +1 more source

A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets

Journal of Forecasting, EarlyView.
ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c$$ c $$ latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley   +1 more source

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer, Kristofer Månsson, Pär Sjölander, Gazi Salah Uddin   +3 more
wiley   +1 more source

Random Integrated Subdata Ensemble Method for Key Variable Selection in Rare Event Setting

Journal of Forecasting, EarlyView.
ABSTRACT We propose a general variable selection procedure to identify key input variables by applying elastic net regression to representative subdata in place of the full sample to select variables. We combine the lists of selected variables from each subdata through ensemble techniques, using the frequency of selecting the variable across different ...
Ching‐Chi Yang, Justin Deng, Lih‐Yuan Deng, Henry Horng‐Shing Lu   +3 more
wiley   +1 more source

Future Range Shifts and Diversity Patterns of Antarctic Lecideoid Lichens Under Climate Change Scenarios

Global Change Biology Communications, EarlyView.
Modeling of current and future spatial distribution of Antarctic lecideoid lichens shows that warming will strongly increase the potential habitat. Under three climate‐change scenarios, most fungal and algal partners are projected to expand inland. Overall, climate change drives widespread range shifts and promotes colonization of interior Antarctica ...
Anna Götz, Mikhail Andreev, Robert R. Junker, Lea Maislinger, Leopoldo G. Sancho, Wolfgang Trutschnig, Ulrike Ruprecht   +6 more
wiley   +1 more source

Hydrogen Halide Gas Sensors: Active Materials, Operation Principles, and Emerging Technologies

Interdisciplinary Materials, EarlyView.
This review considers hydrogen halide (HX) gas sensors across functional materials and principles: acoustic, chemical, optical and nanophotonic. The strong acidity and reactivity of HX gases are discussed as constraints for stability and selectivity of these devices.
Xiuzhen Liu, Nikita Tukachev, Kehang Wang, Dmirty Zhirihin, Yaopeng Cheng, Pavel Krasnov, Mingzhao Song, Soslan Khubezhov, Wenping Yin, Emmanuel Stratakis, Sailing He, Andrey L. Rogach, Sergey Makarov   +12 more
wiley   +1 more source