arXiv subjects

Natalia Soja-Kukieła

Publications and source records attributed to Natalia Soja-Kukieła.

5 recordsLinked to original sources

arXiv2020

Directional phantom distribution functions for~stationary random fields

We give necessary and sufficient conditions for the existence of a phantom distribution function for a stationary random field on a regular lattice. We also introduce a less demanding notion of a directional phantom distribution, with potentially broader area of applicability. Such approach leads to sectorial limit properties, a phenomenon well-known in limit theorems for random fields. An example of a stationary Gaussian random field is provided showing that the two notions do not coincide. Criteria for the existence of the corresponding notions of the extremal index and the sectorial extremal index are also given.

math.PR↗

arXiv2019

On maxima of stationary fields

Let $\{X_{\mathbf{n}} : \mathbf{n}\in\mathbb{Z}^d\}$ be a weakly dependent stationary field with maxima $M_{A} := \sup\{X_{\mathbf{i}} : \mathbf{i}\in A\}$ for finite $A\subset\mathbb{Z}^d$ and $M_{\mathbf{n}} := \sup\{X_{\mathbf{i}} : \mathbf{1} \leq \mathbf{i} \leq \mathbf{n} \}$ for $\mathbf{n}\in\mathbb{N}^d$. In a general setting we prove that $P(M_{(n,n,\ldots, n)} \leq v_n) = \exp(- n^d P(X_{\mathbf{0}} > v_n , M_{A_n} \leq v_n)) + o(1)$, for some increasing sequence of sets $A_n$ of size $ o(n^d)$. For a class of fields satisfying a local mixing condition, including $m$-dependent ones, the theorem holds with a constant finite $A$ replacing $A_n$. The above results lead to new formulas for the extremal index for random fields.

math.PR↗

arXiv2018

Extremes of multidimensional stationary Gaussian random fields

Let $\{X(\mathbf{t}):\mathbf{t}=(t_1, t_2, \ldots, t_d)\in[0,\infty)^d\}$ be a centered stationary Gaussian field with almost surely continuous sample paths, unit variance and correlation function $r$ satisfying conditions $r(\mathbf{t})<1$ for every $\mathbf{t}\neq \mathbf{0}$ and $r(\mathbf{t})=1-\sum_{i=1}^d |t_i|^{α_i} + o(\sum_{i=1}^d |t_i|^{α_i})$, as $\mathbf{t}\to\mathbf{0}$, with constants $α_1, α_2, \ldots, α_d \in(0,2]$. The main result of this contribution is the description of the asymptotic behaviour of $P(\sup\{X(\mathbf{t}): \mathbf{t}\in\mathcal{J}^{\mathbf{x}}_{\mathbf{m}} \}\leqslant u)$, as $u\to\infty$, for some Jordan-measurable sets $\mathcal{J}^{\mathbf{x}}_{\mathbf{m}}$ of volume proportional to $P(\sup\{X(\mathbf{t}):\mathbf{t}\in[0,1]^d\}>u)^{-1}(1+o(1))$.

math.PR↗

arXiv2017

Asymptotics of the order statistics for a process with a regenerative structure

In the paper, a regenerative process $\{X_n:n\in\mathbb{N}\}$ with finite mean cycle length is considered. For~$M_n^{(q)}$ denoting the $q$-th largest value in $\{X_k : 1\leqslant k \leqslant n\}$, we prove that \begin{equation*} \sup_{x\in\mathbb{R}} \left|P\left(M^{(q)}_n\leqslant x\right) - G(x)^n \sum_{k=0}^{q-1}\frac{\left(-\log G(x)^n\right)^k}{k!}γ_{q,k}(x)\right| \to 0,\quad \text{as} \quad n\to\infty, \end{equation*} for $G$ and $γ_{q,k}$ expressed in terms of maxima over the cycle. The result is illustrated with examples.

math.PR↗

arXiv2013

Extremes of homogeneous Gaussian random fields

Let $\{X(s,t):s,t\geqslant 0\}$ be a centered homogeneous Gaussian field with a.s. continuous sample paths and correlation function $r(s,t)=Cov(X(s,t),X(0,0))$ such that \[r(s,t)=1-|s|^{α_1}-|t|^{α_2}+o(|s|^{α_1}+|t|^{α_2}), \quad s,t \to 0,\] with $α_1,α_2\in(0,2],$ and $r(s,t)<1$ for $(s,t)\neq(0,0)$. In this contribution we derive an exact asymptotic expansion (as $u\to \infty$) of $$\mathbb{P}\left(\sup_{(s n_1(u),t n_2(u))\in\left[0,x\right]\times\left[0,y\right]}X(s,t)\leqslant u\right),$$ where $n_1(u)n_2(u)=u^{2/α_1+2/α_2}Ψ(u)$, which holds uniformly for $(x,y) \in [ A , B ]^2$ with $ A , B $ two positive constants and $Ψ$ the survival function of an $N(0,1)$ random variable. We apply our findings to the analysis of asymptotics of extremes of homogeneous Gaussian fields over more complex parameter sets and a ball of random radius. Additionally we determine the extremal index of the discretised random field determined by $X(s,t)$.

math.PR↗