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pro vyhledávání: '"Eichler, Michael"'
Autor:
Leong, Michael, Abdelhalim, Awad, Ha, Jude, Patterson, Dianne, Pincus, Gabriel L., Harris, Anthony B., Eichler, Michael, Zhao, Jinhua
Transit riders' feedback provided in ridership surveys, customer relationship management (CRM) channels, and in more recent times, through social media is key for transit agencies to better gauge the efficacy of their services and initiatives. Gettin
Externí odkaz:
http://arxiv.org/abs/2308.05012
Akademický článek
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Autor:
van Delft, Anne, Eichler, Michael
Publikováno v:
Stochastic Processes and their Applications, Volume 130(6), 2020, Pages 3687-3710
In this article, we prove Herglotz's theorem for Hilbert-valued time series. This requires the notion of an operator-valued measure, which we shall make precise for our setting. Herglotz's theorem for functional time series allows to generalize exist
Externí odkaz:
http://arxiv.org/abs/1801.04262
Autor:
Kopeć, Kamil, Wojasiński, Michał, Eichler, Michael, Genç, Hatice, Friedrich, Ralf P., Stein, René, Singh, Raminder, Alexiou, Christoph, Hlawaty, Hanna, Ciach, Tomasz, Cicha, Iwona
Publikováno v:
In Biomaterials Advances March 2022 134
Akademický článek
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Hawkes (1971) introduced a powerful multivariate point process model of mutually exciting processes to explain causal structure in data. In this paper it is shown that the Granger causality structure of such processes is fully encoded in the correspo
Externí odkaz:
http://arxiv.org/abs/1605.06759
Autor:
van Delft, Anne, Eichler, Michael
Publikováno v:
Electronic Journal of Statistics Volume 12, Number 1 (2018), 107-170
The literature on time series of functional data has focused on processes of which the probabilistic law is either constant over time or constant up to its second-order structure. Especially for long stretches of data it is desirable to be able to we
Externí odkaz:
http://arxiv.org/abs/1602.05125
Autor:
van Delft, Anne, Eichler, Michael
Publikováno v:
Journal of Computational and Graphical Statistics, 28:2, 244-255, 2019
This paper introduces a data-adaptive non-parametric approach for the estimation of time-varying spectral densities from nonstationary time series. Time-varying spectral densities are commonly estimated by local kernel smoothing. The performance of t
Externí odkaz:
http://arxiv.org/abs/1512.00825
Autor:
Eichler, Michael1, Billsberry, Jon2 jbillsbe@uow.edu.au
Publikováno v:
Management Learning. Apr2023, Vol. 54 Issue 2, p244-266. 23p.
Autor:
Eichler, Michael David.
Thesis (M.A. in City and Regional Planning)--University of California, Berkeley, Fall 2005.
Title from PDF title page (viewed Dec. 13, 2007). "Fall 2005." Includes bibliographical references (p. 81-87).
Title from PDF title page (viewed Dec. 13, 2007). "Fall 2005." Includes bibliographical references (p. 81-87).
Externí odkaz:
http://homepage.mac.com/meichler/meichler_thesis_signed.pdf
http://worldcat.org/oclc/183658675/viewonline
http://worldcat.org/oclc/183658675/viewonline