Zobrazeno 1 - 10
of 151
pro vyhledávání: '"Kreiss, Jens‐Peter"'
In this paper we consider the construction of simultaneous confidence bands for the spectral density of a stationary time series using a Gaussian approximation for classical lag-window spectral density estimators evaluated at the set of all positive
Externí odkaz:
http://arxiv.org/abs/2407.12316
Vaccinations against the virus SARS-CoV-2 have proven to be most effective against a severe corona disease. However, a significant minority of people is still critical of such a vaccination or even strictly reject it. One but surely not the only reas
Externí odkaz:
http://arxiv.org/abs/2203.08419
Publikováno v:
In Stochastic Processes and their Applications July 2024 173
Fitting parametric models by optimizing frequency domain objective functions is an attractive approach of parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the (realist
Externí odkaz:
http://arxiv.org/abs/2107.11270
We investigate the problem of estimating the distribution of the individual reproduction number governing the COVID-19 pandemic. Under the assumption that this random variable follows a Negative Binomial distribution, we focus on constructing estimat
Externí odkaz:
http://arxiv.org/abs/2101.07919
Existing frequency domain methods for bootstrapping time series have a limited range. Consider for instance the class of spectral mean statistics (also called integrated periodograms) which includes many important statistics in time series analysis,
Externí odkaz:
http://arxiv.org/abs/1806.06523
The second-order dependence structure of purely nondeterministic stationary process is described by the coefficients of the famous Wold representation. These coefficients can be obtained by factorizing the spectral density of the process. This relati
Externí odkaz:
http://arxiv.org/abs/1712.07371
Publikováno v:
The Annals of Statistics, 2020 Aug 01. 48(4), 2404-2427.
Externí odkaz:
https://www.jstor.org/stable/26931563
Publikováno v:
Annals of Statistics 2011, Vol. 39, No. 4, 2103-2130
We explore the limits of the autoregressive (AR) sieve bootstrap, and show that its applicability extends well beyond the realm of linear time series as has been previously thought. In particular, for appropriate statistics, the AR-sieve bootstrap is
Externí odkaz:
http://arxiv.org/abs/1201.6211
Publikováno v:
Bernoulli, 2018 Nov 01. 23(4B), 2988-3020.
Externí odkaz:
https://www.jstor.org/stable/26491749