  In this article, we study the limit distribution of the least square
estimator, properly normalized, from a regression model in which observations
are assumed to be finite ($\alpha N$) and sampled under two different random
times. Based on the limit behavior of the characteristic function and
convergence result we prove the asymptotic normality for the least square
estimator. We present simulation results to illustrate our theoretical results.
