Results 31 to 40 of about 4,611 (130)

Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease

Annals of Neurology, EarlyView.
Objective Accurate electrode placement and individual stimulation parameters influence the outcomes of subthalamic deep brain stimulation in Parkinson's disease. Neuroimaging‐based models can help evaluate how electrode placement impacts improvement, aiming to reduce the burden of programming.
Patricia Zvarova, Christina van der Linden, Ningfei Li, Konstantin Butenko, Thea Berger, Garance M. Meyer, Ilkem Aysu Sahin, Lukas L. Goede, Bahne H. Bahners, Barbara Hollunder, Till A. Dembek, Andrew R. Pines, Martin Reich, Jens Volkmann, Vincent J. J. Odekerken, Rob M. A. de Bie, Xin Xu, Zhipei Ling, Chen Yao, Andrea A. Kühn, Surjo R. Soekadar, Kerstin Ritter, Michael T. Barbe, Veerle Visser‐Vandewalle, Michael D. Fox, Jan Niklas Petry‐Schmelzer, Nanditha Rajamani, Andreas Horn   +27 more
wiley   +1 more source

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

Machine Learning‐Assisted Design of BaTiO3‐Based Superparaelectric High‐Entropy Ceramics with Superior Energy Storage

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed an adaptive iterative strategy combining machine learning algorithms, domain knowledge, experimental design, and experimental feedback to aim to precisely and quickly discover high‐entropy ceramics with excellent energy storage performance.
Haowen Liu, Xiaoyan Zhang, Zhiyuan Ma, Kailong Ma, Xiwei Qi   +4 more
wiley   +1 more source

AFSR: An Adaptive Factor Score Regression Framework for High-Dimensional Correlation Matrix Estimation

Journal of Mathematics
The accurate estimation of correlation matrices is a foundational challenge in high-dimensional statistics. The sample correlation matrix, while unbiased, suffers from high variance when the number of variables p is large relative to the sample size n ...
Muath Awadalla, Yücel Tandoğdu
doaj   +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

Forecasting House Prices: The Role of Market Interconnectedness

Journal of Forecasting, EarlyView.
ABSTRACT While the existing research uncovers interconnections between various housing markets, it largely ignores the question of whether such linkages can improve house price predictions. To address this issue, we proceed in two steps. First, we forecast disaggregated house price growth rates from Australia and China to determine whether ...
Zac Chen, George Milunovich, Shuping Shi, Ben Wang   +3 more
wiley   +1 more source

The Impact of Uncertainty on Forecasting the US Economy

Journal of Forecasting, EarlyView.
ABSTRACT This paper examines the predictive value of uncertainty measures for key macroeconomic indicators across multiple forecast horizons. We evaluate how different uncertainty proxies—economic policy uncertainty (EPU), VIX, geopolitical risk, and measures of macroeconomic and financial uncertainty—enhance forecast accuracy for industrial production,
Angelica Ghiselli
wiley   +1 more source

Using DSGE and Machine Learning to Forecast Public Debt for France

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos, Thierry Betti, Theophilos Papadimitriou, Amélie Barbier‐Gauchard, Periklis Gogas   +4 more
wiley   +1 more source

Nowcasting World Trade With Machine Learning: A Three‐Step Approach

Journal of Forecasting, EarlyView.
ABSTRACT We nowcast world trade using machine learning, distinguishing between tree‐based methods (random forest and gradient boosting) and their linear‐regression‐based counterparts (macroeconomic random forest and gradient boosting—linear). While much less used in the literature, the latter are found to outperform not only the tree‐based techniques ...
Menzie Chinn, Baptiste Meunier, Sebastian Stumpner   +2 more
wiley   +1 more source