Results 21 to 30 of about 8,329 (109)

General empirical Bayes wavelet methods and exactly adaptive minimax estimation

, 2005
In many statistical problems, stochastic signals can be represented as a sequence of noisy wavelet coefficients. In this paper, we develop general empirical Bayes methods for the estimation of true signal.
Zhang, Cun-Hui
core   +2 more sources

Testing Distributional Granger Causality With Entropic Optimal Transport

Journal of Time Series Analysis, EarlyView.
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley   +1 more source

Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies through Quadratically Constrained Linear Programming

, 2015
A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational studies have been well addressed when there is a single outcome variable ...
Fogarty, Colin B., Small, Dylan S.
core   +2 more sources

Subgroup Identification via Multiple Change Point Detection: Methods and Applications

WIREs Computational Statistics, Volume 18, Issue 2, June 2026.
Subgroup identification methods facilitate the discovery of clinically meaningful subpopulations with differing disease progression, improving personalized risk assessment and treatment strategies. ABSTRACT Subgroup identification is a significant research area in statistics and machine learning, aiming to partition a heterogeneous population into more
Yaguang Li, Jingli Wang, Da Zhao, Jialiang Li   +3 more
wiley   +1 more source

Computers and chess masters: The role of AI in transforming elite human performance

British Journal of Psychology, Volume 117, Issue 2, Page 585-609, May 2026.
Abstract Advances in Artificial Intelligence (AI) have made significant strides in recent years, often supplementing rather than replacing human performance. The extent of their assistance at the highest levels of human performance remains unclear. We analyse over 11.6 million decisions of elite chess players, a domain commonly used as a testbed for AI
Merim Bilalić, Mario Graf, Nemanja Vaci
wiley   +1 more source

Worst-case estimation and asymptotic theory for models with unobservables [PDF]

, 2004
This paper proposes a worst-case approach for estimating econometric models containing unobservable variables. Worst-case estimators are robust against the adverse effects of unobservables.
Esteban-Bravo, Mercedes, Vidal-Sanz, Jose M.   +1 more
core   +1 more source

Testing the isotropy of high energy cosmic rays using spherical needlets

, 2013
For many decades, ultrahigh energy charged particles of unknown origin that can be observed from the ground have been a puzzle for particle physicists and astrophysicists.
Delabrouille, Jacques, Faÿ, Gilles, Kerkyacharian, Gérard, Picard, Dominique   +3 more
core   +4 more sources

Results and Exploratory Biomarker Analyses of a Phase II Study CHANGEABLE: Combination of PD‐1 Inhibitor and Niraparib in GErm‐Line‐mutAted Metastatic Breast Cancer

MedComm, Volume 7, Issue 4, April 2026.
This phase II trial evaluated the efficacy and safety of niraparib combined with HX008 in metastatic breast cancer (MBC) patients with germline DNA damage response (DDR) gene mutations. This chemotherapy‐free regimen demonstrates promising efficacy and a tolerable safety profile in MBC patients with germline DDR mutations, providing a novel therapeutic
Jian Zhang, Yiqun Du, Yanchun Meng, Yizi Jin, Mingxi Lin, Xuchen Shao, Xiaojun Liu, Yuxin Mu, Yun Liu, Zhen Hu   +9 more
wiley   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen, Tord Johan Espe, Kristoffer Grude, Morten Risstad, Rickard Sandberg   +4 more
wiley   +1 more source

Advances in Generative Models for Accelerated Discovery of New Materials

cScience, Volume 2, Issue 1, March 2026.
ABSTRACT The discovery of new materials can drive tremendous social and technological progress. However, the vastness of the material space makes comprehensive exploration computationally infeasible. This paper reviews the inverse design methods of generative models in materials science, aiming to discover customized materials based on specific ...
Yuan Jiang, Jinshan Li, Zhuoyue Yang, Ningxin Hou, Jun Wang, Bin Tang, Ruihao Yuan   +6 more
wiley   +1 more source