=\frac{\theta+1/\gamma_\alpha}{1-1/\gamma_\mu}
\;\;\;\mbox{and}\;\;\;
\langle x \rangle_{\bf B} =\frac{\theta+2/\gamma_\mu}{1-2/\gamma_\alpha}\;,$$ both averages are greater than $\theta$ and coincide only in the limit of relatively small noise amplitudes ($\gamma >>\alpha^2,\mu^2$). Moments $\langle x^n \rangle$ are finite only if $\gamma_\mu>n$ ([**A**]{}) or $\gamma_\alpha>n+1$ ([**B**]{}). In particular, the second moment is $$\label{s2a}
\langle x^2 \rangle_{\bf A} =\frac{\gamma_\alpha(\theta+1/\gamma_\alpha)(\theta+2/\gamma_\alpha)}
                                  {\gamma_\mu(1-1/\gamma_\mu)(1-2/\gamma_\mu)} \;,$$ $$\langle x^2 \rangle_{\bf B} =\frac{\gamma_\mu(\theta+2/\gamma_\mu)(\theta+3/\gamma_\mu)}
                                   {\gamma_\alpha(1-2/\gamma_\alpha)(1-3/\gamma_\alpha)}\;.$$ In model [**A**]{}, increasing(decreasing) amplitude of the additive(multiplicative) noise, increases the width of the distribution, whereas model [**B**]{} presents opposite behavior. Thus, for instance, the additive noise has a confining effect in process [**A**]{}, opposite to the effect observed in processes with null $\theta$[@multi1].

On the other hand, the distribution has a maximum at $$\label{maxima}
x^{max}_{\bf A} =\frac{\theta}{1+1/\gamma_\mu}
\;\;\;\mbox{and}\;\;\;
x^{max}_{\bf B} =\theta+\frac{1}{\gamma_\mu}\;.$$ Notice that the additive noise does not affect the mode, as expected. The most probable value of distribution [**A**]{} shifts to the right with increasing multiplicative amplitude, while in distribution [**B**]{} the opposite tendency occurs. From Eqs. (\[averages\]) and (\[maxima\]), $x^{max}_{\bf A}<\theta_{\bf A}<\langle x\rangle_{\bf A}$, while $\theta_{\bf B}<x^{max}_{\bf B}<\langle x\rangle_{\bf B}$. That is, in model [**A**]{}, the reference value $\theta$ represents a typical value comprised between two central measures, which does not hold in model [**B**]{}. This observation, in 