Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Victor M. Panaretos"'
Autor:
Victor M. Panaretos, Yoav Zemel
This open access book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics in the space of probability measures when endowed with the geometry of optimal transportation. Further to reviewing state-of-the-art aspects, it also
Publikováno v:
Oberwolfach Reports. 16:1697-1735
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53381c572d69d3ca412d3465382f5bb4
https://doi.org/10.4171/owr/2019/28
https://doi.org/10.4171/owr/2019/28
Autor:
Alessia Caponera, Victor M. Panaretos
We consider the problem of estimating the autocorrelation operator of an autoregressive Hilbertian process. By means of a Tikhonov approach, we establish a general result that yields the convergence rate of the estimated autocorrelation operator as a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1f1371cf338a45bea9e19545257ae2c
http://arxiv.org/abs/2202.09287
http://arxiv.org/abs/2202.09287
Publikováno v:
Bernoulli. 27
We develop theory and methodology for the problem of nonparametric registration of functional data that have been subjected to random deformation (warping) of their time scale. The separation of this phase variation ("horizontal" variation) from the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7b1a15ae052118afb46b8f8ce12c896
https://doi.org/10.3150/20-bej1267
https://doi.org/10.3150/20-bej1267
We consider the problem of positive-semidefinite continuation: extending a partially specified covariance kernel from a subdomain $\Omega$ of a rectangular domain $I\times I$ to a covariance kernel on the entire domain $I\times I$. For a broad class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ba3b207e198bad821af857f083e9ff9
Non-parametric inference for functional data over two-dimensional domains entails additional computational and statistical challenges, compared to the one-dimensional case. Separability of the covariance is commonly assumed to address these issues in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c6c182a3580d6fe252b5ee7c65d55bd
Autor:
Yoav Zemel, Victor M. Panaretos
Publikováno v:
An Invitation to Statistics in Wasserstein Space ISBN: 9783030384371
When given measures μ1, …, μN are supported on the real line, computing their Frechet mean \(\bar \mu \) is straightforward (Sect. 3.1.4). This is in contrast to the multivariate case, where, apart from the important yet special case of compatibl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2f0e7510f5aa4ba347e51545030dd1d
https://doi.org/10.1007/978-3-030-38438-8_5
https://doi.org/10.1007/978-3-030-38438-8_5
Autor:
Yoav Zemel, Victor M. Panaretos
Publikováno v:
An Invitation to Statistics in Wasserstein Space ISBN: 9783030384371
If H is a Hilbert space (or a closed convex subspace thereof) and x1, …, xN ∈ H, then the empirical mean \(\overline x_N=N^{-1}\sum x_i\) is the unique element of H that minimises the sum of squared distances from the xi’s.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb410da3979bc055b6924620b6fca4b8
https://doi.org/10.1007/978-3-030-38438-8_3
https://doi.org/10.1007/978-3-030-38438-8_3
Autor:
Yoav Zemel, Victor M. Panaretos
Publikováno v:
An Invitation to Statistics in Wasserstein Space ISBN: 9783030384371
Why is it relevant to construct the Frechet mean of a collection of measures with respect to the Wasserstein metric? A simple answer is that this kind of average will often express a more natural notion of “typical” realisation of a random probab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc222fecd22043784cbad4dc2f721249
https://doi.org/10.1007/978-3-030-38438-8_4
https://doi.org/10.1007/978-3-030-38438-8_4
Autor:
Victor M. Panaretos, Yoav Zemel
Publikováno v:
An Invitation to Statistics in Wasserstein Space ISBN: 9783030384371
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3981fbdef8d795442a03f3760abd0564
https://doi.org/10.1007/978-3-030-38438-8_1
https://doi.org/10.1007/978-3-030-38438-8_1