The talk will focus on the problem of finite-sample null hypothesis significance testing on the mean element of a random variable that takes value in a generic separable Hilbert space. For this purpose, we will present a definition of Hotelling’s T2 statistic that naturally expands to any separable Hilbert space. In detail, we will present a unified framework for making inference on the mean element of Hilbert populations based on Hotelling’s T2 statistic, using a permutation-based testing procedure. We will then present the theoretical properties of the procedure (i.e., finitesample exactness and consistency) and show the explicit form of Hotelling’s T2 statistic in the case of some famous spaces used in functional data analysis li...
This paper reviews the functional aspects of statistical learning theory. The main point under consi...
Motivated by the increasing availability of data of functional nature, we develop a general probabil...
Functional data have been the subject of many research works over the last years. Functional regress...
The talk will focus on the problem of finite-sample null hypothesis significance testing on the mea...
The talk will focus on the problem of finite-sample null hypothesis significance testing on the mea...
We address the problem of finite-sample null hypothesis significance testing on the mean element of ...
While Hotelling’s T2 statistic is traditionally defined as the Mahalanobis distance between the sam...
21 pages, 5 figuresInternational audienceWe consider the problem of testing for the nullity of condi...
Many problems in unsupervised learning require the analysis of features of probability distributions...
We study the asymptotic behavior of the logistic classifier in an abstract Hilbert space and require...
International audienceWe provide a generalization of Hotelling's Theorem that en- ables inference (i...
Multivariate functional data is defined as an element of a direct sum of Hilbert spaces, H(p) = H1⊕...
We study the problem of testing the null hypothesis that X and Y are conditionally independent given...
This book is an introduction to the theory of Hilbert space, a fundamental tool for non-relativistic...
This paper reviews the functional aspects of statistical learning theory. The main point under con-s...
This paper reviews the functional aspects of statistical learning theory. The main point under consi...
Motivated by the increasing availability of data of functional nature, we develop a general probabil...
Functional data have been the subject of many research works over the last years. Functional regress...
The talk will focus on the problem of finite-sample null hypothesis significance testing on the mea...
The talk will focus on the problem of finite-sample null hypothesis significance testing on the mea...
We address the problem of finite-sample null hypothesis significance testing on the mean element of ...
While Hotelling’s T2 statistic is traditionally defined as the Mahalanobis distance between the sam...
21 pages, 5 figuresInternational audienceWe consider the problem of testing for the nullity of condi...
Many problems in unsupervised learning require the analysis of features of probability distributions...
We study the asymptotic behavior of the logistic classifier in an abstract Hilbert space and require...
International audienceWe provide a generalization of Hotelling's Theorem that en- ables inference (i...
Multivariate functional data is defined as an element of a direct sum of Hilbert spaces, H(p) = H1⊕...
We study the problem of testing the null hypothesis that X and Y are conditionally independent given...
This book is an introduction to the theory of Hilbert space, a fundamental tool for non-relativistic...
This paper reviews the functional aspects of statistical learning theory. The main point under con-s...
This paper reviews the functional aspects of statistical learning theory. The main point under consi...
Motivated by the increasing availability of data of functional nature, we develop a general probabil...
Functional data have been the subject of many research works over the last years. Functional regress...