Estimation of Self-Exciting Point Processes from Time-Censored Data

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In this paper, we perform the joint study of scale -invariant operators and self -similar processes of complex order. More precisely, we introduce general families of scale -invariant complex -order fractional -derivation and integration operators by const ...

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Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

Blood pressure (BP) is a crucial biomarker giving valuable information regarding cardiovascular diseases but requires accurate continuous monitoring to maximize its value. In the effort of developing non-invasive, non-occlusive and continuous BP monitoring ...

This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

We introduce the elliptical Ornstein-Uhlenbeck (OU) process, which is a generalisation of the well-known univariate OU process to bivariate time series. This process maps out elliptical stochastic oscillations over time in the complex plane, which are obse ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

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 kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

We present FITCOV an approach for accurate estimation of the covariance of two-point correlation functions that requires fewer mocks than the standard mock-based covariance. This can be achieved by dividing a set of mocks into jackknife regions and fitting ...

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 ...

We present TimeEvolver, a program for computing time evolution in a generic quantum system. It relies on well-known Krylov subspace techniques to tackle the problem of multiplying the exponential of a large sparse matrix iH, where His the Hamiltonian, with ...

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results i ...

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle l ...

This thesis focuses on non-parametric covariance estimation for random surfaces, i.e.~functional data on a two-dimensional domain. Non-parametric covariance estimation lies at the heart of functional data analysis, and considerations of statistical and com ...

Estimation of Self-Exciting Point Processes from Time-Censored Data

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Spectral Estimators for High-Dimensional Matrix Inference

Complex-order scale-invariant operators and self-similar processes

Detecting whether a stochastic process is finitely expressed in a basis

Stochastic adaptive resampling for the estimation of discrete choice models

Positive Definite Completions and Continuous Graphical Models

Timing and spatial selection bias in rapid extreme event attribution

Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

The elliptical Ornstein-Uhlenbeck process

Quantization for Decentralized Learning Under Subspace Constraints

An equilibrated flux a posteriori error estimator for defeaturing problems

Density estimation in RKHS with application to Korobov spaces in high dimensions

The two-point correlation function covariance with fewer mocks

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

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

TimeEvolver: A program for time evolution with improved error bound

Universal and adaptive methods for robust stochastic optimization

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

The Debiased Spatial Whittle likelihood

Covariance Estimation for Random Surfaces beyond Separability