Results 91 to 100 of about 1,306,091 (274)

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

Enhancing Volatility Prediction: A Wavelet‐Based Hierarchical Forecast Reconciliation Approach

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting realized volatility (RV) has been widely studied, with numerous techniques developed to enhance predictive accuracy. Among these techniques, the use of RV decompositions based on intraday asset returns has been applied. However, the use of a frequency‐based decomposition, which provides unique insights into the dynamics of RV ...
Adam Clements, Ajith Perera
wiley   +1 more source

Bootstrapping smooth conformal defects in Chern-Simons-matter theories

Journal of High Energy Physics
Abstract The expectation value of a smooth conformal line defect in a CFT is a conformal invariant functional of its path in space-time. For example, in large N holographic theories, these fundamental observables are dual to the open-string partition function in AdS.
Barak Gabai, Amit Sever, De-liang Zhong
openaire   +3 more sources

Evaluating Forecasts at Multiple Horizons: An Extension of the Diebold–Mariano Approach

Journal of Forecasting, EarlyView.
ABSTRACT Forecast accuracy tests are fundamental tools for comparing competing predictive models. The widely used Diebold–Mariano (DM) test assesses whether differences in forecast errors are statistically significant. However, its standard form is limited to pairwise comparisons at a single forecast horizon.
Andrew Grant, Matus Mrazik, Steve Satchell   +2 more
wiley   +1 more source

The Role of Price‐Volatility Cojumps in Volatility Forecasting

Journal of Futures Markets, EarlyView.
ABSTRACT This paper investigates whether simultaneous jumps in prices and volatility improve volatility forecasting. Using up‐to‐date high‐frequency S&P 500 and VIX data, we identify price‐volatility cojumps at the intraday granularity and construct upside, downside, and asymmetric measures.
Kefu Liao
wiley   +1 more source

Creation of a Landslide Susceptibility Map Using Short‐Term Data From the July 2018 Heavy Rainfall in Southern Hiroshima Prefecture

Geological Journal, EarlyView.
This work advances landslide susceptibility mapping by incorporating short‐term trigger data with landscape susceptibility mapping. We also examine the importance of downsampling, watershed delineation and geospatial correlations in evaluating outcomes.
Kanta Kotsugi, Ting Liu, Toshiro Hata, Rebecca Muenich   +3 more
wiley   +1 more source

Central Limit Theorem and Bootstrap Approximation in High Dimensions: Near $1/\sqrt{n}$ Rates via Implicit Smoothing [PDF]

, 2020
Miles E. Lopes
openalex   +1 more source

Deep learning‐based prediction of cervical lymph node metastasis and genetic alterations from whole‐slide images of thyroid cancer frozen sections

Interdisciplinary Medicine, EarlyView.
Deep learning models accurately predict cervical lymph node metastasis and key genetic mutations (BRAF/TERT) directly from thyroid cancer frozen sections. This AI‐driven pipeline provides a rapid real‐time tool to guide intraoperative surgical decisions, helping to optimize surgical extent and prevent both over‐ and under‐treatment without the need for
Mingxing Qiu, Jinbang Li, Shujing Guo, Danyi Li, Chengyu Lu, Chenglong Zhao, Jiankun He, Hanxi Wang, Qiao Bai, Aihetaimu Aimaier, Zhijian Cen, Rui Mao, Zimo Ye, Jiaxue Zang, Zhining Zhuo, Lijuan Qu, Yueping Liu, Jing Cui, Xiaohong Yao, Xiuwu Bian, Li Liang   +20 more
wiley   +1 more source

Joint Estimation and Bandwidth Selection in Partially Parametric Models

Journal of Applied Econometrics, EarlyView.
ABSTRACT We propose a single‐step approach to estimating a model with both a known nonlinear parametric component and an unknown nonparametric component. We study the large sample behavior of a simultaneous optimization routine that estimates both the parameter vector of the parametric component and the bandwidth vector used to smooth the unknown ...
Daniel J. Henderson, Nadine McCloud, Christopher F. Parmeter   +2 more
wiley   +1 more source

A Consistent Heteroskedasticity‐Robust LM‐Type Specification Test for Semiparametric Models

Journal of Applied Econometrics, EarlyView.
ABSTRACT This article develops a heteroskedasticity‐robust Lagrange Multiplier‐type specification test for semiparametric regression models. The test is able to detect a wide class of deviations from the null hypothesis. The test statistic is based on the estimates from the restricted semiparametric model, can be computed in a regression‐based way, and
Ivan Korolev
wiley   +1 more source