Let $Y$ be a Gaussian vector of $\mathbb{R}^n$ of mean $s$ and diagonal covariance matrix $Γ$. Our aim is to estimate both $s$ and the entries $σ_i=Γ_{i,i}$, for $i=1,...,n$, on the basis of the observation of two independent copies of $Y$. Our approach is free of any prior assumption on $s$ but requires that we know some upper bound $γ$ on the ratio $\max_iσ_i/\min_iσ_i$. For example, the choice $γ=1$ corresponds to the homoscedastic case where the components of $Y$ are assumed to have common (unknown) variance. In the opposite, the choice $γ>1$ corresponds to the heteroscedastic case where the variances of the components of $Y$ are allowed to vary within some range. Our estimation strategy is based on model selection. We consider a family $\{S_m\timesΣ_m, m\in\mathcal{M}\}$ of parameter sets where $S_m$ and $Σ_m$ are linear spaces. To each $m\in\mathcal{M}$, we associate a pair of estimators $(\hat{s}_m,\hatσ_m)$ of $(s,σ)$ with values in $S_m\timesΣ_m$. Then we design a model selection procedure in view of selecting some $\hat{m}$ among $\mathcal{M}$ in such a way that the Kullback risk of $(\hat{s}_{\hat{m}},\hatσ_{\hat{m}})$ is as close as possible to the minimum of the Kullback risks among the family of estimators $\{(\hat{s}_m,\hatσ_m), m\in\mathcal{M}\}$. Then we derive uniform rates of convergence for the estimator $(\hat{s}_{\hat{m}},\hatσ_{\hat{m}})$ over Hölderian balls. Finally, we carry out a simulation study in order to illustrate the performances of our estimators in practice.