Copula-based models provide a great deal of ‡exibility in modelling multivariate distributions, allowing the researcher to specify the models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. In addition to ‡exibility, this often also facilitates estimation of the model in stages, reducing the computational burden. Thi...

This paper reports on the application to field measurements of time series methods developed on the basis of the theory of deterministic chaos. The major difficulties are pointed out that arise when the data cannot be assumed to be purely deterministic and the potential that remains in this situation is discussed. For signals with weakly nonlinear structure, the presence of nonlinearity in a ge...

Qualitative evaluation and comparison of changes of indications of objects having different nature is used by designers, managers, people making decisions (PMD) and experts to make the decisions more reasonable. For suport of such activity on the analysis of changes of data connected with certain dates and time intervals, models of fuzzy time series are applied. In this article a model of fuzzy...

We adapt smoothing methods to histogram-valued time series (HTS) by introducing a barycentric histogram that emulates the "average" operation, which is the key to any smoothing filter. We show that, due to its linear properties, only the Mallows-barycenter is acceptable if we wish to preserve the essence of any smoothing mechanism. We implement a barycentric exponential smoothing to forecast th...

This paper reviews the analysis of the threshold autoregressive, smooth threshold autoregressive, and Markov switching autoregressive models from the Bayesian perspective. For each model we start by describing a baseline model and discussing possible extensions and applications. Then we review the choice of prior, inference, tests against the linear hypothesis, and conclude with models selectio...

History UPDATE 1 (18 AUG 2011) – Updated according to the formatting requests of the publisher (Peter Lang Verlag), e.g. Abstract and Erklärung are deleted, etc. UPDATE 2 (27 AUG 2011) – Updated based on native-English proofreading. Acknowledgments It is hardly possible to recollect the names of all the persons who directly or indirectly inspired my research through discussions, conference talk...

We study the robustness of block resampling procedures for time series. We first derive a set of formulas to characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile breakdown point. A similar robustness problem arises in relation to data-driven methods for selecting the block size in applications. This renders inference based ...

In knowledge discovery from time series the goal is to detect interesting patterns in the series that may help a human to better recognize the regularities in the observed variables and thereby improve the understanding of the system. Computer programs are very good in number crunching, but knowledge arises only in the head of a human. Ideally, knowledge discovery algorithms therefore use time ...

This article gives an overview on nonparametric modelling of nonstationary time series and estimation of their time-changing spectral content by modern denoising (smoothing) methods. For the modelling aspect localized decompositions such as various local Fourier (spectral) representations are discussed, among which wavelet and local cosine bases are most prominent ones. For the estimation of th...