Complete convergence and complete moment convergence for arrays of Rowwise asymptotically almost negatively associated random variables under the sub-linear expectations

In this article, we investigate the complete convergence and complete moment convergence for maximal partial sums of asymptotically almost negatively associated random variables under the sublinear expectations. The results obtained in the article are the extensions of the complete convergence and complete moment convergence under classical linear expectation space.

Strong Consistency of Estimators in a Partially Linear Model with Asymptotically Almost Negatively Associated Errors

This paper studies a heteroscedastic partially linear regression model in which the errors are asymptotically almost negatively associated (AANA, in short) random variables with not necessarily identical distribution and zero mean. Under some mild conditions, we establish the strong consistency of least squares estimators, weighted least squares estimators, and the ultimate weighted least squares estimators for the unknown parameter, respectively. In addition, the strong consistency of the estimator for nonparametric component is also investigated. The results derived in the paper include the corresponding ones of independent random errors and some dependent random errors as special cases. At last, two simulations are carried out to study the numerical performance of the strong consistency for least squares estimators and weighted least squares estimators of the unknown parametric and nonparametric components in the model.