Weak convergence of nonparametric estimators of the multidimensional and multidimensional-multivariate renewal functions on Skorohod topology spaces

Statistical Inference for Stochastic Processes - This paper deals with the weak convergence of nonparametric estimators of the multidimensional and multidimensional-multivariate renewal functions...     

A nonparametric test for conditional symmetry in nonstationary and absolutely regular dynamical models

In this Note, we reconsider the test for symmetry of the errors distribution in a class of heteroscedastic models proposed by Ngatchou-Wandji (2009). In the new study, the observations, as well as the errors, are not necessarily stationary but are required to be absolutely regular.     

Testing nonstationary and absolutely regular nonlinear time series models

Statistical Inference for Stochastic Processes - We study some general methods for testing the goodness-of-fit of a general nonstationary and absolutely regular nonlinear time series model. These...     

A Cramér-von Mises test for symmetry of the error distribution in asymptotically stationary stochastic models

This paper has to do with a Cramér-von Mises test for symmetry of the error distribution in a class of absolutely regular and non-necessarily stationary heteroscedastic models. The test statistic is based on the empirical characteristic function. Its convergence, as well as that of the residual-based empirical distribution function are established. From these results, the null cumulative distribution function of the test statistic is approximated. A simulation experiment shows that the test performs well on the examples tested.