Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Alois Kneip"'
Publikováno v:
Biometrics. 77:839-851
Multivariate functional data are becoming ubiquitous with advances in modern technology and are substantially more complex than univariate functional data. We propose and study a novel model for multivariate functional data where the component proces
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f39c7df651ac3b6650fab1848e67ad2
https://doi.org/10.1111/biom.13340
https://doi.org/10.1111/biom.13340
Autor:
Dominik Poß, Alois Kneip, Hedwig Eisenbarth, Lisa Feldman Barrett, Tor D. Wager, Dominik Liebl
Publikováno v:
Journal of the Royal Statistical Society Series B: Statistical Methodology. 82:1115-1140
Summary Predicting scalar outcomes by using functional predictors is a classical problem in functional data analysis. In many applications, however, only specific locations or time points of the functional predictors have an influence on the outcome.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c7e45a18b4b0dc9ebf0223f853b0e37
https://doi.org/10.1111/rssb.12386
https://doi.org/10.1111/rssb.12386
Publikováno v:
Econometric Theory. 37:537-572
The Malmquist index gives a measure of productivity in dynamic settings and has been widely applied in empirical work. The index is typically estimated using envelopment estimators, particularly data envelopment analysis (DEA) estimators. Until now,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f293d4faca3a41174400f896a89ccba6
https://doi.org/10.1017/s0266466620000237
https://doi.org/10.1017/s0266466620000237
Publikováno v:
Journal of the American Statistical Association. 116:1383-1401
We consider the problem of estimating the covariance function of functional data which are only observed on a subset of their domain, such as fragments observed on small intervals or related types ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9210b7293c2f7829da439de49ad193e
https://doi.org/10.1080/01621459.2020.1723597
https://doi.org/10.1080/01621459.2020.1723597
We study the consistency of the estimator in spatial regression with partial differential equa-tion (PDE) regularization. This new smoothing technique allows to accurately estimate spatial fields over complex two-dimensional domains, starting from no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::807565c07e4729966869f2bf93a218cc
http://hdl.handle.net/11311/1205242
http://hdl.handle.net/11311/1205242
While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this paper, a novel method for estimating panel models with multiple structu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92f95b1d23ab587194b3bc3cfacea86e
http://arxiv.org/abs/2109.10950
http://arxiv.org/abs/2109.10950
Publikováno v:
Econometrics and Statistics. 21:112-113
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5da6952a950d9818972129a1f6b1804
https://doi.org/10.1016/j.ecosta.2021.11.003
https://doi.org/10.1016/j.ecosta.2021.11.003
Autor:
Alois Kneip, Heiko Wagner
Publikováno v:
Computational Statistics & Data Analysis. 138:49-63
Registration aims to decompose amplitude and phase variation of samples of curves. Phase variation is captured by warping functions which monotonically transform the domains. Resulting registered curves should then only exhibit amplitude variation. M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f067f9608866256ecbd5b9776b6d84d4
https://doi.org/10.1016/j.csda.2019.03.004
https://doi.org/10.1016/j.csda.2019.03.004
Autor:
Alois Kneip, Dominik Liebl
Publikováno v:
Ann. Statist. 48, no. 3 (2020), 1692-1717
We propose a new reconstruction operator that aims to recover the missing parts of a function given the observed parts. This new operator belongs to a new, very large class of functional operators which includes the classical regression operators as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f940f2993036b43617beccd6f87544b5
https://projecteuclid.org/euclid.aos/1594972835
https://projecteuclid.org/euclid.aos/1594972835
Publikováno v:
Functional and High-Dimensional Statistics and Related Fields ISBN: 9783030477554
We numerically study the bias and the mean square error of the estimator in Spatial Regression with Partial Differential Equation (SR-PDE) regularization. SR-PDE is a novel smoothing technique for data distributed over two-dimensional domains, which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5c811498f84e24838f13660b55aac5b
https://doi.org/10.1007/978-3-030-47756-1_3
https://doi.org/10.1007/978-3-030-47756-1_3