Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

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In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

We consider the problem of nonparametric estimation of the drift and diffusion coefficients of a Stochastic Differential Equation (SDE), based on n independent replicates {Xi(t) : t is an element of [0 , 1]}13 d B(t), where alpha is an element of {0 , 1} a ...

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model ...

We summarize what we consider to be the two main limitations of the "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event" (Schmidli et al. 2022). First, the authors did not give detailed guidance on how to choose an appropriate esti ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. ...

This paper studies the infinite-width limit of deep linear neural networks (NNs) initialized with random parameters. We obtain that, when the number of parameters diverges, the training dynamics converge (in a precise sense) to the dynamics obtained from a ...

DNA mechanics plays a crucial role in many biological processes, including nucleosome positioning and protein-DNA interactions. It is believed that nature employs epigenetic modifications in DNA to further regulate gene expression. Moreover, double-strande ...

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

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Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Nonparametric estimation for SDE with sparsely sampled paths: An FDA perspective

An equilibrated flux a posteriori error estimator for defeaturing problems

Spectral Estimators for High-Dimensional Matrix Inference

Distributional Regression and Autoregression via Optimal Transport

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

OASIS: An integrated optimisation framework for activity scheduling

A Shape Derivative Approach to Domain Simplification

Infinite-width limit of deep linear neural networks

cgNA+: A sequence-dependent coarse-grain model of double-stranded nucleic acids