Results 41 to 50 of about 12,928 (140)
Journal of Time Series Analysis, EarlyView.ABSTRACT We consider the problem of sequential (online) estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that certain CUSUM‐type statistics attain the minimax optimal rates for localizing the change point.
Annika Hüselitz, Housen Li, Axel Munk
wiley +1 more source
Optimal Estimation and Prediction for Dense Signals in High-Dimensional Linear Models [PDF]
, 2012 Estimation and prediction problems for dense signals are often framed in terms of minimax problems over highly symmetric parameter spaces. In this paper, we study minimax problems over l2-balls for high-dimensional linear models with Gaussian predictors.
Dicker, Lee
core
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
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 +4 more
wiley +1 more source
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
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Generative Models for Crystalline Materials
Advanced Materials, Volume 38, Issue 18, 25 March 2026.Generative machine learning models are increasingly used in crystalline materials design. This review outlines major generative approaches and assesses their strengths and limitations. It also examines how generative models can be adapted to practical applications, discusses key experimental considerations for evaluating generated structures, and ...
Houssam Metni +15 more
wiley +1 more source
, 2020 We present a unified study of first and second order necessary and sufficient optimality conditions for minimax and Chebyshev optimisation problems with cone constraints. First order optimality conditions for such problems can be formulated in several different forms: in terms of a linearised problem, in terms of Lagrange multipliers (KKT-points), in ...
openaire +3 more sources
Standardized maximim D-optimal designs for enzyme kineticinhibition models [PDF]
, 2017 Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters.An alternative is the maximin approach that allows the user to specify a range of values for each parameter ofinterest.
Chen, Ping-Yang +3 more
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Advanced Science, Volume 13, Issue 14, 9 March 2026.We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu +7 more
wiley +1 more source
Pinsker estimators for local helioseismology
, 2016 A major goal of helioseismology is the three-dimensional reconstruction of the three velocity components of convective flows in the solar interior from sets of wave travel-time measurements.
Fournier, D. +3 more
core +1 more source