We establish an invariance principle for a general class of stationary random fields indexed by Zd, under Hannan’s condition generalized to Zd. To do so we first establish a uniform integrability result for stationary orthomartingales, and second we establish a coboundary decomposition for...

We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable...

This work deals with the stationary bootstrap of Politis and Romano (1994) for theψ-weakly dependent sequences proposed by Doukhan and Louhichi (1999), establishing strong consistency of the bootstrap sample variance and the bootstrap sample mean under the dependence structure for the observed...

Empirical and sequential empirical copula processes play a central role for statistical inference on copulas. However, as pointed out by Johan Segers [J. Segers, Asymptotics of empirical copula processes under non-restrictive smoothness assumptions, Bernoulli 18 (3) (2012) 764–782] the usual...

We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set...

Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series requires specifying a fixed block length as a tuning parameter for setting confidence regions. This aspect can be difficult and impacts coverage accuracy. As an alternative, this paper proposes a new...

We derive the asymptotical distributions of two-sample U-statistics and two-sample empirical U-quantiles in the case of weakly dependent data. Our results apply to observations that can be represented as functionals of absolutely regular processes, including e.g. many classical time series...

In this paper an overview is given over recent theoretical developments in autoregressive count time series. The focus is on generalized autoregressive models where the autoregressive structure is incorporated via a link function. Starting from an ordinary autoregressive model the difficulties...

The paper deals with two methods of solving optimization programs where uncertainties occur: stochastic (in particular chance-constrained) programming and robust programming. We review briefly how these two methods deal with uncertainty and what approximations are commonly used. Furthermore, we...

In this paper a new mixing condition for sequences of random variables is considered. This mixing condition is termed ã-mixing. Whereas mixing conditions such as á-mixing are typically defined in terms of entire ó-fields of sets generated by random variables in the distant...