Results 141 to 150 of about 123,594 (283)

Intracranial electroencephalographic connectivity analysis to localize epileptogenic networks: Systematic review and meta‐analysis from ILAE Epilepsy Surgery Networks Task Force

Epilepsia, EarlyView.
Abstract Intracranial electroencephalographic (iEEG) connectivity analysis is a promising method to localize epileptic networks and guide surgical planning in focal drug‐resistant epilepsy. Despite numerous studies exploring its utility, the added value of iEEG connectivity over standard clinical presurgical evaluation remains unclear.
Nishant Sinha, Daniel J. Zhou, Victoria L. Morgan, Dario J. Englot, Leonardo Bonilha, Milan Brázdil, Xiaosong He, Riki Masumoto, Quy Cao, Terence J. O'Brien, Chengyuan Wu, Erik Kaestner, Carrie McDonald, Ezequiel Gleichgerrcht, Aileen McGonigal, Kathryn A. Davis   +15 more
wiley   +1 more source

DETECTING LEVEL SHIFTS IN THE PRESENCE OF CONDITIONAL HETEROSCEDASTICITY [PDF]

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series.
Daniel Peña, Esther Ruiz, M. Angeles Carnero   +2 more
core  

Covariance Structure Modeling of Engineering Demand Parameters in Cloud‐Based Seismic Analysis

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Probabilistic seismic demand modeling aims to estimate structural demand as a function of ground motion intensity—a critical stage in seismic risk assessment. Although many models exist to describe the structural demand, few consider the covariance among engineering demand parameters, potentially overlooking a key factor in improving the ...
Archie Rudman, Enrico Tubaldi, Roberto Gentile, John Douglas   +3 more
wiley   +1 more source

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +1 more source

Parental and Peer Relationships and Their Impact on Symptom Severity in Adolescent Patients With Anorexia Nervosa

European Eating Disorders Review, EarlyView.
ABSTRACT Objective Perceived parental relationship characteristics, such as maternal overprotection, rejection or neglect, and peer victimisation, are suggested to be more common in patients with anorexia nervosa (AN) than in healthy controls. This study compares parental and peer relationships in adolescent patients with AN to those in a clinical ...
Armita Tschitsaz, Andrea M. Schumacher, Stefan Lerch, Andrea Wyssen, Marialuisa Cavelti, Ines Mürner‐Lavanchy, Julian Koenig, Michael Kaess, Franziska Schlensog‐Schuster   +8 more
wiley   +1 more source

Research on the Impact of Power Industry Agglomeration on the Development of Energy Industry—Empirical Evidence From China's Provincial Panel

Energy Science &Engineering, EarlyView.
ABSTRACT Under the background of Global Climate Governance and “double carbon” goals, power industry agglomeration is an important path to promote the development of energy industry, and its mechanism and impact effect need to be further explored.
Tanbo Zhu, Wei Bu
wiley   +1 more source

Local Polynomial Regression and Filtering for a Versatile Mesh‐Free PDE Solver

International Journal for Numerical Methods in Fluids, EarlyView.
A high‐order, mesh‐free finite difference method for solving differential equations is presented. Both derivative approximation and scheme stabilisation is carried out by parametric or non‐parametric local polynomial regression, making the resulting numerical method accurate, simple and versatile. Numerous numerical benchmark tests are investigated for
Alberto M. Gambaruto
wiley   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Intraday Functional PCA Forecasting of Cryptocurrency Returns

Journal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

Term Spread Volatility as a Leading Indicator of Economic Activity

Journal of Forecasting, EarlyView.
ABSTRACT In this paper, we examine the macroeconomic predictive power of the volatility of the US Treasury yield curve slope (term spread volatility). Our forecasting exercise shows that US term spread volatility has significant predictive power for US industrial production and employment growth.
Anastasios Megaritis, Dimitrios Bakas, Theodora Bermpei, Athanasios Triantafyllou   +3 more
wiley   +1 more source