Results 81 to 90 of about 514 (177)

Projective Hessian and Sasakian manifolds

, 2018
The Hessian geometry is the real analogue of the Kähler one. Sasakian geometry is an odd-dimensional counterpart of the Kähler geometry. In the paper, we study the connection between projective Hessian and Sasakian manifolds analogous to the one between Hessian and Kähler manifolds.
openaire   +2 more sources

Penalized Convex Estimation in Dynamic Location Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper studies L1$$ {L}^1 $$‐penalized estimation for location models yt=mt+ϵt$$ {y}_t={m}_t+{\epsilon}_t $$, where mt$$ {m}_t $$ is defined by a possibly non‐Markovian recursion and ϵt$$ {\epsilon}_t $$ is a martingale difference sequence with possibly time‐varying conditional variance.
Reda Alami Chentoufi
wiley   +1 more source

Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)

Journal of Time Series Analysis, EarlyView.
ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley   +1 more source

Parametric Time‐Variation in the Unconditional Volatility: Estimation and Inference

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose modeling time‐variation in the unconditional volatility by augmenting the standard GARCH model by a deterministic time‐varying intercept. The model, called the additive time‐varying (ATV‐)GARCH model, can be interpreted as a reduced form of a model including covariates and can be derived from a multiplicative decomposition of ...
Niklas Ahlgren, Alexander Back, Timo Teräsvirta   +2 more
wiley   +1 more source

Reinforcement Learning for Jump‐Diffusions, With Financial Applications

Mathematical Finance, EarlyView.
ABSTRACT We study continuous‐time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump‐diffusion processes. We formulate an entropy‐regularized exploratory control problem with stochastic policies to capture the exploration–exploitation balance essential for RL.
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
wiley   +1 more source

Stochastic Gradient Descent in High Dimensions for Multi‐Spiked Tensor PCA

Communications on Pure and Applied Mathematics, Volume 79, Issue 10, Page 2291-2369, October 2026.
ABSTRACT We study the high‐dimensional dynamics of online stochastic gradient descent (SGD) for the multi‐spiked tensor model. This multi‐index model arises from the tensor principal component analysis (PCA) problem with multiple spikes, where the goal is to estimate the unknown signal vectors within the N$N$‐dimensional unit sphere through maximum ...
Gérard Ben Arous, Cédric Gerbelot, Vanessa Piccolo   +2 more
wiley   +1 more source

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm

Magnetic Resonance in Medicine, Volume 96, Issue 3, Page 1323-1332, September 2026.
ABSTRACT Purpose The Unadjusted Langevin Algorithm (ULA) in combination with diffusion models can generate high quality MRI reconstructions with uncertainty estimation from highly undersampled k‐space data. However, sampling methods such as diffusion posterior sampling (DPS) or likelihood annealing suffer from long reconstruction times and the need for
Moritz Blumenthal, Tina Holliber, Jonathan I. Tamir, Martin Uecker   +3 more
wiley   +1 more source

On Topology of Compact Hessian Manifolds

We investigate the global topological constraints and structural properties of compact Hessian manifolds. By establishing novel fibration and splitting theorems, we confirm Chern's conjecture on the vanishing of the Euler characteristic for this class of affine manifolds.
openaire   +2 more sources

Model‐Agnostic Influential Outlier Metric

Stat, Volume 15, Issue 3, September 2026.
ABSTRACT The influential outlier metric (IOM) provides model‐agnostic influential outlier detection. We define influence of an observation using a combination of SHapley Additive exPlanation (SHAP) values and the residual. Both are transformed using normalizing flows, changing their respective measures to Gaussian distributions.
Colin C. Jones, David A. Campbell
wiley   +1 more source

Monge-Ampère equations on compact Hessian manifolds

ANNALI SCUOLA NORMALE SUPERIORE - CLASSE DI SCIENZE
We consider degenerate Monge-Ampère equations on compact Hessian manifolds. We establish compactness properties of the set of normalized quasi-convex functions and show local and global comparison principles for twisted Monge-Ampère operators. We then use the Perron method to solve Monge-Ampère equations whose RHS involves an arbitrary probability ...
Guedj, Vincent, Tô, Tat Dat
openaire   +3 more sources