In this paper we propose to continue in the same research line initiated by Pronzato and Thierry [A minimum-entropy estimator for regression problems with unknown distribution of observation errors], [Entropy minimization of parameter estimator with unknown distribution of observation erros], recent works inspired in the minimum-entropy estimation have been published by De la Rosa and Fleury [On the Kernel selection for Minimum-Entropy estimation], [Minimum-Entropy, pdf approximation and Kernel selection for measurement estimation] in the instrumentation framework. An statistical model has been established to represent some instrumental signals, similarly, some limited hypothesis over such a model have been made. In fact, we assume limited knowledge of the noise or external perturbations distribution that interact into the system. The use of robust estimators in such situations is very helpful, since the real systems are always exposed to continuous perturbations of unknown nature. Some applications where the last is true are: medical instrumentation, industrial processes, in telecommunications among others. Some results of new minimum-entropy estimators for linear and nonlinear models are presented, such results complement those presented by Pronzato and Thierry.