R\leq s_{j})_{j=1,\dots ,m}$ be of order $p=0.2$ or 0.3, in such a way that $\left\{ R\leq s_{j+1}\right\} $ is observed in experiments performed under the conditional distribution of $R$ given $\left\{ R\leq s_{j}\right\} $, and in a way which makes $\alpha $ recoverable by a rather small number of such probabilities $\mathbb{P}(R\leq s_{j+1}\mid
R\leq s_{j})$ making use of .

From the formal decomposition in , a practical experimental scheme can be deduced. Its form is given in algorithm \[Split\].

\[Split\] **Initialization**

Fix

-   the first tested level $s_1$ (ideally the $p-$quantile of the distribution of $R$);

-   the number $K$ of trials to be performed at each iteration.

**First step**

-   $K$ trials are performed at level $s_1$. The observations are the indicators of failure $Y_{1,1},\dots,Y_{1,K}$, where $Y_{1,i} = \mathds{1}(R_{1,i}<s_1)$ of distribution $\mathcal{B}\left( \mathbb{P}(R \le s_1)\right)$.

-   Determination of $s_2$, $p-$quantile of the truncated distribution $R \mid R \le s_1$.

**Iteration $j=2$ to $m$**

-   $K$ trials are performed at level $s_j$ under the truncated distribution of $R \mid R \le s_{j-1}$ resulting to observations $Y_{j,1},\dots,Y_{j,k} \sim \mathcal{B}\left( \mathbb{P}(R \le s_j\mid R \le s_{j-1})\right)$.

-   Determination of $s_{j+1}$, the $p-$quantile of $R \mid R \le s_{j}$.

The last estimated quantile 