Méthodes de rééchantillonnage pour l'estimation de variance

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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 ...

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 ...

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 ...

Is it possible to detect if the sample paths of a stochastic process almost surely admit a finite expansion with respect to some/any basis? The determination is to be made on the basis of a finite collection of discretely/noisily observed sample paths. We ...

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 ...

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Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the G ...

This thesis concerns the theory of positive-definite completions and its mutually beneficial connections to the statistics of function-valued or continuously-indexed random processes, better known as functional data analysis. In particular, it dwells upon ...

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Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

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Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

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Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

Méthodes de rééchantillonnage pour l'estimation de variance

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OASIS: An integrated optimisation framework for activity scheduling

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

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

Detecting whether a stochastic process is finitely expressed in a basis

Timing and spatial selection bias in rapid extreme event attribution

Real-Time Localization for Closed-Loop Control of Assistive Furniture

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Stochastic adaptive resampling for the estimation of discrete choice models

State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

Positive Definite Completions and Continuous Graphical Models

Parametric Estimation For Functional Autoregressive Processes On The Sphere

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Momentum-Based Policy Gradient with Second-Order Information

Financial Risk Management with Machine Learning

Audio-Visual Object Classification For Human-Robot Collaboration

Memory of Motion for Initializing Optimization in Robotics

Data-driven and Safe Networked Control with Applications to Microgrids

Quantified Effects of Multiple Parameters on Inputs and Potential Sources of Microplastics from a Typical River Flowing into the Sea

Experimental assessment of traffic density estimation at link and network level with sparse data

Sparsely Observed Functional Time Series: Theory and Applications