In this paper, we propose an estimator of the generalized maximum mean discrepancy between several probability distributions, constructed by modifying a naive estimator. Asymptotic normality is obtained for both under equality these distributions and alternative hypothesis, so allowing to achieve k-sample test distributions. A simulation study that allows compare proposed existing ones provided.

Abstract: Poisson regression models for count variables have been utilized in many applications. However, in many problems overdispersion and zero-inflation occur. In this paper we study regression models associated with the generalized Poisson distribution (Consul (1989)). These regression models which have been used for about 15 years do not belong to the class of generalized linear models co...

Asymptotic normality as well as some weak invariance principles for bonus sums and waiting times in an extended coupon collector's problem are considered and incorporated in the study of the asymptotic distribution theory of estimators of (finite) population totals in successive sub-sampling (or multistage sampling) with varying probabilities (without replacements). Some applications of these t...

There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality is established. A real data example is used to illustrate the proposed estimation procedure.

In this paper, we study the asymptotic distribution of a simple two-stage (Hannan-Rissanen-type) linear estimator for stationary invertible vector autoregressive moving average (VARMA) models in the echelon form representation. General conditions for consistency and asymptotic normality are given. A consistent estimator of the asymptotic covariance matrix of the estimator is also provided, so t...