# $\ell^1$ penalization

### joint work with K. Bertin and V. Rivoirard

We consider here an example of $\ell^1$ regularization for density estimation: the adaptive Dantzig estimate. We assume that the unknown density $f$ belongs to $L^2$ et one tries to estimate it by a linear combination $$f_{\lambda} = \sum_{k=1}^p \lambda_k \phi_k$$ where $\{ \phi_k \}_{1 \leq k \leq p}$ is a dictionary of functions in $L^2\cup L^{\infty}$. From the observations $X_1,\ldots,X_n$, one computes empirical scalar products $$\widehat{\beta}_k= \frac{1}{n} \sum_{i=1}^n \phi_k(X_i).$$ and associated precisions $\eta_k$. The Dantzig estimate $f_{\widehat{\lambda}}$ is defined through the vector $\widehat{\lambda}$: $$\widehat{\lambda} = \mathop{\mathrm{argmin}} \|\lambda \|_1 \quad\text{under}\quad \forall 1 \leq k \leq p, |\langle f_{\lambda}, \phi_k \rangle- \widehat{\beta}_k| \leq \eta_k.$$

In this article, we show that $\eta_k$ can be chosen essentially as $$\eta_k = \sqrt{2\gamma\log p} \frac{ \widehat{\sigma}_k}{\sqrt{n}} + \frac{7}{3} \gamma \log p \frac{\|\phi_k\|_{\infty}}{n}$$ where $\widehat{\sigma}^2_k$ is a natural estimate of the variance of $\widehat{\beta}_k$. We explain also the links between this estimate and the Laso estimate. We also show that our penalty is well calibrated: if the parameter $\gamma$ is chosen larger than $1$ then, under mild assumptions on the dictionary, oracle inequalities can be obtained whild if it is chosen smaller than $1$ there are always cases when this estimate could not satifies those oracle inequalities.

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