Least-square estimators in linear regression models under negatively superadditive dependent random observations

In this article, we study the asymptotic behaviour of least-square estimator in a linear regression model based on random observation instances. We provide mild assumptions moments and dependence structure randomly spaced observations residuals under which is strongly consistent. particular, consider instances that are negatively superadditive dependent within each other, while for merely assume they generated by some continuous function. complement our findings with simulation providing insights finite sample properties.