In this talk, we consider a change-point modeling and inference problems in generalized Ornstein-Uhlenbeck and Cox-Ingersoll-Ross processes with possible change-points. A salient feature of this investigation consists in the fact that, as opposed to the results in recent literature, the dimensions of the drift parameter is considered as unknown. Further, we weaken some assumptions, in recent literature, underlying the asymptotic optimality of some estimators of the drift parameter. We also prove that the proposed methods improve the goodness-of-fit and the predictive accuracy. Finally, we propose shrinkage estimators (SEs) for the drift parameter and prove that SEs dominate the unrestricted estimator.