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

Limit distribution of the least square estimator with observations sampled at random times driven by standard Brownian motion