Recovering covariance from functional fragments

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

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. ...

The limitations of 2D microscopy constrain our ability to observe and understand tissue-wide networks that are, by nature, 3-dimensional. Optical projection tomography (OPT) enables the acquisition of large volumes (ranging from micrometres to centimetres) ...

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 ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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 ...

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

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 ...

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligen ...

Single-cell sequencing (sc-seq) provides a species agnostic tool to study cellular processes. However, these technologies are expensive and require sufficient cell quantities and biological replicates to avoid artifactual results. An option to address thes ...

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 ...

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

We compute three-term semiclassical asymptotic expansions of counting functions and Riesz-means of the eigenvalues of the Laplacian on spheres and hemispheres, for both Dirichlet and Neumann boundary conditions. Specifically for Riesz-means we prove upper ...

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 ...

We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of luminous red galaxies (LRGs) data collected during the initial 2 months of operat ...

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 ...

How can we discern whether the covariance operator of a stochastic pro-cess is of reduced rank, and if so, what its precise rank is? And how can we do so at a given level of confidence? This question is central to a great deal of methods for functional dat ...

The aim of this paper is to define a nonlinear least squares estimator for the spectral parameters of a spherical autoregressive process of order 1 in a parametric setting. Furthermore, we investigate on its asymptotic properties, such as weak consistency ...

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

Some implications of absolute geometries in the description of complex systems dynamics, at various scale resolutions are highlighted. In such context, by means of an analytic geometry of 2 x 2 matrices, a generalization of the standard velocities space in ...

Recovering covariance from functional fragments

Chattez avec Graph Search

Positive Definite Completions and Continuous Graphical Models

A Shape Derivative Approach to Domain Simplification

Optical imaging of the small intestine immune compartment across scales

Stochastic adaptive resampling for the estimation of discrete choice models

Bayes-optimal Learning of Deep Random Networks of Extensive-width

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

Towards a multiscale point cloud structural similarity metric

The two-point correlation function covariance with fewer mocks

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

Evaluation of genetic demultiplexing of single-cell sequencing data from model species

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

Robust discrete choice models with t-distributed kernel errors

Semiclassical Estimates for Eigenvalue Means of Laplacians on Spheres

Covariance Estimation for Random Surfaces beyond Separability

Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

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

Testing For The Rank Of A Covariance Operator

Parametric Estimation For Functional Autoregressive Processes On The Sphere

Functional data analysis with rough sample paths?

Some Physical Implications Of Absolute Geometries In The Description Of Complex Systems Dynamics