Modern Stochastics: Theory and Applications

In this paper, we introduce a family of semi-parametric estimators for the positive extreme value index γ, parameterized in two tuning parameters. The asymptotic normality of the introduced estimators is proved. It is shown that the partial case of newly introduced estimators (a subfamily with one tuning parameter) has quite good asymptotic properties and dominates several previously introduced estimators. Small Monte-Carlo simulations are included. Also, the performance of this parameterized subfamily of estimators is illustrated for pair exchange ratio data sets.

We prove a limit theorem for paths of random walks with n steps in ${\mathbb{R}^{d}}$ as n and d both go to infinity. For this, the paths are viewed as finite metric spaces equipped with the ${\ell _{p}}$-metric for $p\in [1,\infty )$. Under the assumptions that all components of each step are uncorrelated, centered, have finite $2p$-th moments, and are identically distributed, we show that such random metric space converges in probability to a deterministic limit space with respect to the Gromov-Hausdorff distance. This result generalises earlier work by Kabluchko and Marynych [Ann. Inst. H. Poincaré Probab. Statist. 60(4): 2945–2974, 2024] for $p=2$.

Multisets are like sets, except that they can contain multiple copies of their elements. If there are ${n_{i}}$ copies of i, $1\le i\le t$, in multiset ${M_{t}}$, then there are $\left(\genfrac{}{}{0.0pt}{}{{n_{1}}+\cdots +{n_{t}}}{{n_{1}},\dots ,{n_{t}}}\right)$ possible permutations of ${M_{t}}$. Knuth showed how to factor any multiset permutation into cycles. For fixed ${n_{i}}$, $i\ge 1$, we show how to adapt the Chinese restaurant process, which generates random permutations on n elements with weighting ${\theta ^{\# \hspace{0.1667em}\mathrm{cycles}}}$, $\theta \gt 0$, sequentially for $n=1,2,\dots $, to the multiset case, where we fix the ${n_{i}}$ and build permutations on ${M_{t}}$ sequentially for $t=1,2,\dots $. The number of cycles of a multiset permutation chosen uniformly at random, i.e. $\theta =1$, has distribution given by the sum of independent negative hypergeometric distributed random variables. For all $\theta \gt 0$, and under the assumption that ${n_{i}}=O(1)$, we show a central limit theorem as $t\to \infty $ for the number of cycles.

In this paper, we consider a modified version of a well-known submartingale condition for the weak convergence of probability measures, adapted to the semi-Markov case. In this setting, it is convenient to work with an embedded Markov chain and the filtration generated by jump times. We demonstrate that a straightforward restatement of the classical result is not valid, and that an additional condition is required.