Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Rainer Dahlhaus"'
Publikováno v:
Bernoulli 25, no. 2 (2019), 1013-1044
In this paper, some general theory is presented for locally stationary processes based on the stationary approximation and the stationary derivative. Laws of large numbers, central limit theorems as well as deterministic and stochastic bias expansion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8464f35ba9f67ab762ed7c699c50a6b7
https://projecteuclid.org/euclid.bj/1551862842
https://projecteuclid.org/euclid.bj/1551862842
Publikováno v:
Journal of Time Series Analysis. 38:225-242
Hawkes (1971a) introduced a powerful multivariate point process model of mutually exciting processes to explain causal structure in data. In this article, it is shown that the Granger causality structure of such processes is fully encoded in the corr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06b6fd51e6645ab4272092a3245d23ce
https://doi.org/10.1111/jtsa.12213
https://doi.org/10.1111/jtsa.12213
Autor:
Rainer Dahlhaus, Stefan Richter
Publikováno v:
Ann. Statist. 47, no. 4 (2019), 2145-2173
We propose an adaptive bandwidth selector via cross validation for local M-estimators in locally stationary processes. We prove asymptotic optimality of the procedure under mild conditions on the underlying parameter curves. The results are applicabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e93b781eefc612e659d01409c8095f52
http://arxiv.org/abs/1705.10046
http://arxiv.org/abs/1705.10046
Autor:
Rainer Dahlhaus, Jan C. Neddermeyer
Publikováno v:
Journal of Financial Econometrics. 12:174-212
A technique for on-line estimation of spot volatility for high-frequency data is developed. The algorithm works directly on the transaction data and updates the volatility estimate immediately after the occurrence of a new transaction. Furthermore, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00e0c3ec4dee7bd5129ded1fc6c23d05
http://hdl.handle.net/10.1093/jjfinec/nbt008
http://hdl.handle.net/10.1093/jjfinec/nbt008
Publikováno v:
Journal of Time Series Analysis. 33:13-31
A classical model in time series analysis is a stationary process superposed by one or several deterministic sinusoidal components. Di erent methods are applied to estimate the frequency (w) of those components such as Least Squares Estimation and th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::101bb22af2bce71de46f31c0d6ffc72a
https://doi.org/10.1111/j.1467-9892.2011.00736.x
https://doi.org/10.1111/j.1467-9892.2011.00736.x
Publikováno v:
Journal of the Royal Statistical Society Series B: Statistical Methodology. 68:721-746
SummaryOver recent decades increasingly more attention has been paid to the problem of how to fit a parametric model of time series with time-varying parameters. A typical example is given by autoregressive models with time-varying parameters. We pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::700b05f01fe4f71170c88b2b4fd0f707
https://doi.org/10.1111/j.1467-9868.2006.00564.x
https://doi.org/10.1111/j.1467-9868.2006.00564.x
Autor:
Carl H. Lücking, Matthias Winterhalder, Bernhard Hellwig, Rainer Dahlhaus, Jens Timmer, Martin Peifer, B. Guschlbauer, Bjoern Schelter, Michael Eichler
Publikováno v:
Journal of Neuroscience Methods, 152, 210-219. Elsevier Science
One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity. When applying multivariate time series analysis techniques to neural signals, detection of directed relationships, which can be de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33138de335a1688210cff7085b02f4e8
https://doi.org/10.1016/j.jneumeth.2005.09.001
https://doi.org/10.1016/j.jneumeth.2005.09.001
Publikováno v:
Statistics
Statistics, 2017, 51 (1), pp.61--83. ⟨10.1080/02331888.2016.1266985⟩
Statistics, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2017, 51 (1), pp.61--83. ⟨10.1080/02331888.2016.1266985⟩
Statistics, 2017, 51 (1), pp.61--83. ⟨10.1080/02331888.2016.1266985⟩
Statistics, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2017, 51 (1), pp.61--83. ⟨10.1080/02331888.2016.1266985⟩
International audience; A new model for time series with a specific oscillation pattern is proposed. The model consists of a hidden phase process controlling the speed of polling and a nonparametric curve characterizing the pattern, leading together
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b360e8eed7fa386f827107fac522bb6
http://arxiv.org/abs/1412.4912
http://arxiv.org/abs/1412.4912
Autor:
Rainer Dahlhaus
Publikováno v:
Journal of the Korean Statistical Society. 40:379-381
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a406e96494b1d63a5bea9f40039f865
https://doi.org/10.1016/j.jkss.2011.07.004
https://doi.org/10.1016/j.jkss.2011.07.004
Autor:
Michael H. Neumann, Rainer Dahlhaus
Publikováno v:
Stochastic Processes and their Applications. 91:277-308
We fit a class of semiparametric models to a nonstationary process. This class is parametrized by a mean function µ( · ) and a p-dimensional function theta ( · ) = (theta(1)( · ) , ..., theta(p) ( · ))´ that parametrizes the time-varying spectr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c235269da620c304d6e463fe2a7a0bc9
https://doi.org/10.1016/s0304-4149(00)00060-0
https://doi.org/10.1016/s0304-4149(00)00060-0