Results 131 to 140 of about 64,866 (212)

SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT. [PDF]

Ann Appl Stat
Deng Y, Zeng D, Wang Y.
europepmc   +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

Covariate selection strategies and estimands - a review of current practice of risk factor analysis from a causal perspective. [PDF]

BMC Med Res Methodol
Reinhammar R, Waernbaum I.
europepmc   +1 more source

Research on Deformation Prediction of Small Interval Tunnel Based on Machine Learning and Numerical Simulation

Energy Science &Engineering, EarlyView.
This study leverages machine learning algorithms—specifically artificial neural networks (ANN) and genetic programming (GP)—to forecast and analyze variations in vault settlement measurements during excavation of small interval tunnel. A settlement prediction model was developed and validated through comparative analysis with regression to evaluate the
Wenjie Zhai, Jia Xue, Yongjie Fu, Beichen Yu, Yan Zhu, Man Yuan, Mingbo Chi, Zhentao Li   +7 more
wiley   +1 more source

Renewable Energy Use, Agricultural Productivity, and Economic Expansion Impact on Environmental Sustainability in India

Energy Science &Engineering, EarlyView.
ABSTRACT This research aims to explore the effects of agricultural productivity and the use of renewable energy sources on India's CO2 emissions while taking economic expansion into concern based on data during 1985–2022. This study applies the autoregressive distributed lag (ARDL) technique, dynamic‐ordinary least square (DOLS), fully‐modified ...
Palanisamy Manigandan, Radulescu Magdalena, Duraisamy Pachiyappan, Nadia Sha, Marian Cristescu   +4 more
wiley   +1 more source

Mixing Behavior of Natural Gas and Hydrogen in a High‐Efficiency Vane (HEV) Static Mixer

Energy Science &Engineering, EarlyView.
Static mixers play a crucial role in the safe transport of hydrogen‐blended natural gas. Computational fluid dynamics (CFD) was employed to investigate the effects of structural parameters of the HEV static mixer on the mixing behavior and homogeneity of natural gas and hydrogen. The modeling approach was well validated against experimental data.
Xiang Zhou, Jun Zheng, Weiqing Xu, Dewen Kong, Yan Shi, Maolin Cai   +5 more
wiley   +1 more source

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches

Journal of Forecasting, EarlyView.
ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann, Mawuli Segnon, Timo Wiedemann   +2 more
wiley   +1 more source

Coherent Forecasting of Realized Volatility

Journal of Forecasting, EarlyView.
ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
wiley   +1 more source

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 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