## On Wu’s Inequality and the Poisson-Föllmer Process.

Resumen: In the discrete setting the Poisson distribution is a ubiquitous object, as the Gaussian distribution is in the Euclidean setting. In spite of that, it does not satisfy Gross’ log-Sobolev inequality. Nevertheless, Bobkov and Ledoux were able to prove that it satisfies a “modified” version of it, which was subsequently reinforced by Wu. In the first part of this talk we will exhibit a new stochastic proof of Wu’s modified log-Sobolev, via an entropy-minimizing process constructed by Klartag and Lehec, which we call the Poisson-Föllmer process. We will also see how this stochastic...

Read More## On Bernoulli Trials with unequal harmonic success probability.

Resumen: A Bernoulli scheme with unequal harmonic success probabilities is investigated, together with some if its natural extensions. The study includes the number of successes over some time window, the times to (between) successive successes and the time to the first success. Large sample asymptotics, parameter estimation, and relations to Sibuya distributions and Yule–Simon distributions are briefly discussed. Stirling numbers play a key role in the analysis. This toy model is relevant in several applications including reliability, species sampling problems, record values breaking and...

Read More## Sharp large deviation principles for descents of random permutations.

Resumen: A permutation $\pi_n$ of size $n$ is said to have a descent at position $k$ if $\pi_n(k) > \pi_n(k + 1)$. We define $D_n$ as the number of descents of $\pi_n$, and $D_n’$ as the number of descents of $\pi_n^{-1}$, the inverse permutation of $\pi_n$. In this talk, I will present recent developments regarding the sharp large deviations for $D_n$ and for the pair $(D_n, D_n’)$, along with other probabilistic results when $\pi_n$ is taken uniformly at random from $S_n$, the set of permutations of size $n$. Work in collaboration with B. Bercu, M. Bonnefont, and A....

Read More## Optimal methods for combining discrete p-values.

Abstract: Combining the significance of multiple experiments regarding the same scientific hypothesis is a crucial method for global hypothesis testing, with applications in meta-analysis, signal detection, and other data-integrative studies. Such procedures consider a group of p-values, $P_1, \cdots, P_n$, to form a summary statistic to determine the overall evidence against a global null hypothesis. Mathematical studies often assume that the underlying statistics are continuous and independent, due to their homogeneous and straightforward mathematical structure. In reality, however, data...

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