On a Characterization of Idempotent Distributions on Discrete Fields and on the Field of <Emphasis Type="Italic">p</Emphasis>-Adic Numbers

We prove the following theorem. Let X be a discrete field, and \(\xi \) and \(\eta \) be independent identically distributed random variables with values in X and distribution \(\mu \). The random variables \(S=\xi +\eta \) and \(D=(\xi -\eta )^2\) are independent if and only if \(\mu \) is an idempotent distribution. A similar result is also proved in the case when \(\xi \) and \(\eta \) are independent identically distributed random variables with values in the field of p-adic numbers \({\mathbf {Q}}_p\), where \(p>2\), assuming that the distribution \(\mu \) has a continuous density.     

Generalizations of an Expansion Formula for Top to Random Shuffles

In the top to random shuffle, the first \({a}\) cards are removed from a deck of \({n}\) cards \({12 \cdots n}\) and then inserted back into the deck. This action can be studied by treating the top to random shuffle as an element \({B_a}\), which we define formally in Section 2, of the algebra \({{\mathbb{Q}[S_n]}}\). For \({a = 1}\), Garsia in “On the powers of top to random shuffling” (2002) derived an expansion formula for \({{B^k_1}}\) for \({{k \leq n}}\), though his proof for the formula was non-bijective. We prove, bijectively, an expansion formula for the arbitrary finite product \({B_{a1} B_{a2} \cdots B_{ak}}\) where \({a_{1}, \cdots , a_{k}}\) are positive integers, from which an improved version of Garsia’s aforementioned formula follows. We show some applications of this formula for \({B_{a1} B_{a2} \cdots B_{ak}}\), which include enumeration and calculating probabilities. Then for an arbitrary group \({G}\) we define the group of \({G}\)-permutations \({{S^G_n} := {G \wr S_n}}\) and further generalize the aforementioned expansion formula to the algebra \({{\mathbb{Q} [ S^G_n ]}}\) for the case of finite \({G}\), and we show how other similar expansion formulae in \({{\mathbb{Q} [S_n]}}\) can be generalized to \({{\mathbb{Q} [S^G_n]}}\).     

The First Passage Time of a Stable Process Conditioned to Not Overshoot

Consider a stable Lévy process \(X=(X_t,t\ge 0)\) and let \(T_{x}\), for \(x>0\), denote the first passage time of \(X\) above the level \(x\). In this work, we give an alternative proof of the absolute continuity of the law of \(T_{x}\) and we obtain a new expression for its density function. Our constructive approach provides a new insight into the study of the law of \(T_{x}\). The random variable \(T_{x}^{0}\), defined as the limit of \(T_{x}\) when the corresponding overshoot tends to \(0\), plays an important role in obtaining these results. Moreover, we establish a relation between the random variable \(T_{x}^{0}\) and the dual process conditioned to die at \(0\). This relation allows us to link the expression of the density function of the law of \(T_{x}\) presented in this paper to the already known results on this topic.     

Two Novel Characterizations of Self-Decomposability on the Half-Line

Two novel characterizations of self-decomposable Bernstein functions are provided. The first one is purely analytic, stating that a function \(\varPsi \) is the Bernstein function of a self-decomposable probability law \(\pi \) on the positive half-axis if and only if alternating sums of \(\varPsi \) satisfy certain monotonicity conditions. The second characterization is of probabilistic nature, showing that \(\varPsi \) is a self-decomposable Bernstein function if and only if a related d-variate function \(C_{\psi ,d}\), \(\psi :=\exp (-\varPsi )\), is a d-variate copula for each \(d \ge 2\). A canonical stochastic construction is presented, in which \(\pi \) (respectively \(\varPsi \)) determines the probability law of an exchangeable sequence of random variables \(\{U_k\}_{k\in {\mathbb {N}}}\) such that \((U_1,\ldots ,U_d) \sim C_{\psi ,d}\) for each \(d \ge 2\). The random variables \(\{U_k\}_{k\in {\mathbb {N}}},\) are i.i.d. conditioned on an increasing Sato process whose law is characterized by \(\varPsi \). The probability law of \(\{U_k\}_{k \in {\mathbb {N}}}\) is studied in quite some detail.     

CLT for Random Walks of Commuting Endomorphisms on Compact Abelian Groups

Let \(\mathcal S\) be an abelian group of automorphisms of a probability space \((X, {\mathcal A}, \mu )\) with a finite system of generators \((A_1, \ldots , A_d).\) Let \(A^{{\underline{\ell }}}\) denote \(A_1^{\ell _1} \ldots A_d^{\ell _d}\), for \({{\underline{\ell }}}= (\ell _1, \ldots , \ell _d).\) If \((Z_k)\) is a random walk on \({\mathbb {Z}}^d\), one can study the asymptotic distribution of the sums \(\sum _{k=0}^{n-1} \, f \circ A^{\,{Z_k(\omega )}}\) and \(\sum _{{\underline{\ell }}\in {\mathbb {Z}}^d} {\mathbb {P}}(Z_n= {\underline{\ell }}) \, A^{\underline{\ell }}f\), for a function f on X. In particular, given a random walk on commuting matrices in \(SL(\rho , {\mathbb {Z}})\) or in \({\mathcal M}^*(\rho , {\mathbb {Z}})\) acting on the torus \({\mathbb {T}}^\rho \), \(\rho \ge 1\), what is the asymptotic distribution of the associated ergodic sums along the random walk for a smooth function on \({\mathbb {T}}^\rho \) after normalization? In this paper, we prove a central limit theorem when X is a compact abelian connected group G endowed with its Haar measure (e.g., a torus or a connected extension of a torus), \(\mathcal S\) a totally ergodic d-dimensional group of commuting algebraic automorphisms of G and f a regular function on G. The proof is based on the cumulant method and on preliminary results on random walks.     

Functionals of a Lévy Process on Canonical and Generic Probability Spaces

We develop an approach to Malliavin calculus for Lévy processes from the perspective of expressing a random variable \(Y\) by a functional \(F\) mapping from the Skorohod space of càdlàg functions to \(\mathbb {R}\), such that \(Y=F(X)\) where \(X\) denotes the Lévy process. We also present a chain-rule-type application for random variables of the form \(f(\omega ,Y(\omega ))\). An important tool for these results is a technique which allows us to transfer identities proved on the canonical probability space (in the sense of Solé et al.) associated to a Lévy process with triplet \((\gamma ,\sigma ,\nu )\) to an arbitrary probability space \((\varOmega ,\mathcal {F},\mathbb {P})\) which carries a Lévy process with the same triplet.     

Extremal eigenvalues and eigenvectors of deformed Wigner matrices

We consider random matrices of the form \(H = W + \lambda V, \lambda \in {\mathbb {R}}^+\), where \(W\) is a real symmetric or complex Hermitian Wigner matrix of size \(N\) and \(V\) is a real bounded diagonal random matrix of size \(N\) with i.i.d. entries that are independent of \(W\). We assume subexponential decay of the distribution of the matrix entries of \(W\) and we choose \(\lambda \sim 1\), so that the eigenvalues of \(W\) and \(\lambda V\) are typically of the same order. Further, we assume that the density of the entries of \(V\) is supported on a single interval and is convex near the edges of its support. In this paper we prove that there is \(\lambda _+\in {\mathbb {R}}^+\) such that the largest eigenvalues of \(H\) are in the limit of large \(N\) determined by the order statistics of \(V\) for \(\lambda >\lambda _+\). In particular, the largest eigenvalue of \(H\) has a Weibull distribution in the limit \(N\rightarrow \infty \) if \(\lambda >\lambda _+\). Moreover, for \(N\) sufficiently large, we show that the eigenvectors associated to the largest eigenvalues are partially localized for \(\lambda >\lambda _+\), while they are completely delocalized for \(\lambda <\lambda _+\). Similar results hold for the lowest eigenvalues.     

Almost sure limit theorems of extremes of complete and incomplete samples of stationary sequences

Let \((X_{n}^{\ast})\) be an independent identically distributed random sequence. Let \(M_{n}^{\ast}\) and \(m_{n}^{\ast}\) denote, respectively, the maximum and minimum of \(\{X_{1}^{\ast},\cdots,X_{n}^{\ast}\}\). Suppose that some of the random variables \(X_1^{\ast},X_2^{\ast},\cdots\) can be observed and let \(\widetilde{M}_n^{\ast}\) and \(\widetilde{m}_n^{\ast}\) denote, respectively, the maximum and minimum of the observed random variables from the set \(\{X_1^{\ast},\cdots,X_n^{\ast}\}\). In this paper, we consider the asymptotic joint limiting distribution and the almost sure limit theorems related to the random vector \((\widetilde{M}_n^{\ast}, \widetilde{m}_n^{\ast}, M_n^{\ast}, m_n^{\ast})\). The results are extended to weakly dependent stationary Gaussian sequences.     

Cutoff on all Ramanujan graphs

We show that on every Ramanujan graph \({G}\), the simple random walk exhibits cutoff: when \({G}\) has \({n}\) vertices and degree \({d}\), the total-variation distance of the walk from the uniform distribution at time \({t=\frac{d}{d-2} \log_{d-1} n + s\sqrt{\log n}}\) is asymptotically \({{\mathbb{P}}(Z > c \, s)}\) where \({Z}\) is a standard normal variable and \({c=c(d)}\) is an explicit constant. Furthermore, for all \({1 \leq p \leq \infty}\), \({d}\)-regular Ramanujan graphs minimize the asymptotic \({L^p}\)-mixing time for SRW among all \({d}\)-regular graphs. Our proof also shows that, for every vertex \({x}\) in \({G}\) as above, its distance from \({n-o(n)}\) of the vertices is asymptotically \({\log_{d-1} n}\).     

Excited Random Walks with Non-nearest Neighbor Steps

Let W be an integer-valued random variable satisfying \(E[W] =: \delta \ge 0\) and \(P(W<0)>0\), and consider a self-interacting random walk that behaves like a simple symmetric random walk with the exception that on the first visit to any integer \(x\in \mathbb Z\), the size of the next step is an independent random variable with the same distribution as W. We show that this self-interacting random walk is recurrent if \(\delta \le 1\) and transient if \(\delta >1\). This is a special case of our main result which concerns the recurrence and transience of excited random walks (or cookie random walks) with non-nearest neighbor jumps.     

Weak law of large numbers for linear processes

We establish sufficient conditions for the Marcinkiewicz–Zygmund type weak law of large numbers for a linear process \({\{X_k:k\in\mathbb Z\}}\) defined by \({X_k=\sum_{j=0}^\infty\psi_j\varepsilon_{k-j}}\) for \({k\in\mathbb Z}\), where \({\{\psi_j:j\in\mathbb Z\}\subset\mathbb R}\) and \({\{\varepsilon_k:k\in\mathbb Z\}}\) are independent and identically distributed random variables such that \({{x^p\Pr\{|\varepsilon_0| > x\}\to 0}}\) as \({{x\to \infty}}\) with \({1 < p < 2}\) and \({E \varepsilon_0=0}\). We use an abstract norming sequence that does not grow faster than \({n^{1/p}}\) if \({\sum|\psi_j| < \infty}\). If \({\sum|\psi_j|=\infty}\), the abstract norming sequence might grow faster than \({n^{1/p}}\) as we illustrate with an example. Also, we investigate the rate of convergence in the Marcinkiewicz–Zygmund type weak law of large numbers for the linear process.     

Coalescece ad Meetig Times o $$$$-Block Markov Chais

We consider finite-state, discrete-time, mixing Markov chains \((V,P)\), where \(V\) is the state space and \(P\) is the transition matrix. To each such chain \((V,P)\), we associate a sequence of chains \((V_n,P_n)\) by coding trajectories of \((V,P)\) according to their overlapping \(n\)-blocks. The chain \((V_n,P_n)\), called the \(n\)-block Markov chain associated with \((V,P)\), may be considered an alternate version of \((V,P)\) having memory of length \(n\). Along such a sequence of chains, we characterize the asymptotic behavior of coalescence times and meeting times as \(n\) tends to infinity. In particular, we define an algebraic quantity \(L(V,P)\) depending only on \((V,P)\), and we show that if the coalescence time on \((V_n,P_n)\) is denoted by \(C_n\), then the quantity \(\frac{1}{n} \log C_n\) converges in probability to \(L(V,P)\) with exponential rate. Furthermore, we fully characterize the relationship between \(L(V,P)\) and the entropy of \((V,P)\).     

Percolation of random nodal lines

We prove a Russo-Seymour-Welsh percolation theorem for nodal domains and nodal lines associated to a natural infinite dimensional space of real analytic functions on the real plane. More precisely, let \(U\) be a smooth connected bounded open set in \(\mathbf{R}^{2}\) and \(\gamma, \gamma '\) two disjoint arcs of positive length in the boundary of \(U\). We prove that there exists a positive constant \(c\), such that for any positive scale \(s\), with probability at least \(c\) there exists a connected component of the set \(\{x\in \smash{\bar{U}},\ f(sx) > 0\} \) intersecting both \(\gamma \) and \(\gamma '\), where \(f\) is a random analytic function in the Wiener space associated to the real Bargmann-Fock space. For \(s\) large enough, the same conclusion holds for the zero set \(\{x\in \smash{\bar{U}},\ f(sx) = 0\} \). As an important intermediate result, we prove that sign percolation for a general stationary Gaussian field can be made equivalent to a correlated percolation model on a lattice.     

Transience and Recurrence of Random Walks on Percolation Clusters in an Ultrametric Space

We study transience and recurrence of simple random walks on percolation clusters in the hierarchical group of order N, which is an ultrametric space. The connection probability on the hierarchical group for two points separated by distance k is of the form \(c_k/N^{k(1+\delta )}, \delta >0\), with \(c_k=C_0+C_1\log k+C_2k^\alpha \), non-negative constants \(C_0, C_1, C_2\), and \(\alpha >0\). Percolation occurs for \(\delta <1\), and for the critical case, \(\delta =1\), \(\alpha >0\) and sufficiently large \(C_2\). We show that in the case \(\delta <1\) the walk is transient, and in the case \(\delta =1,C_2>0,\alpha >0\) there exists a critical \(\alpha _\mathrm{c}\in (0,\infty )\) such that the walk is recurrent for \(\alpha <\alpha _\mathrm{c}\) and transient for \(\alpha >\alpha _\mathrm{c}\). The proofs involve ultrametric random graphs, graph diameters, path lengths, and electric circuit theory. Some comparisons are made with behaviours of simple random walks on long-range percolation clusters in the one-dimensional Euclidean lattice.     

Orthogonal projections of cubes and regular simplices into a plane

We show that for every \({k\ge 2}\) and \({n\ge k}\), there is an \({n}\)-dimensional unit cube in \({\mathbb{R}^n}\) which is mapped to a regular \({2k}\)-gon by an orthogonal projection in \({\mathbb{R}^n}\) onto a \({2}\)-dimensional subspace. Moreover, by increasing dimension \({n}\), arbitrary large regular \({2k}\)-gon can be obtained in such a way. On the other hand, for every \({m\ge 3}\) and \({n\ge m-1}\), there is an \({n}\)-dimensional regular simplex of unit edge in \({\mathbb{R}^n}\) which is mapped to a regular \({m}\)-gon by an orthogonal projection onto a plane. Moreover, contrary to the cube case, arbitrary small regular \({m}\)-gon can be obtained in such a way, by increasing dimension \({n}\).     

Heavy-Tailed Random Walks on Complexes of Half-Lines

We study a random walk on a complex of finitely many half-lines joined at a common origin; jumps are heavy-tailed and of two types, either one-sided (towards the origin) or two-sided (symmetric). Transmission between half-lines via the origin is governed by an irreducible Markov transition matrix, with associated stationary distribution \(\mu _k\). If \(\chi _k\) is 1 for one-sided half-lines k and 1 / 2 for two-sided half-lines, and \(\alpha _k\) is the tail exponent of the jumps on half-line k, we show that the recurrence classification for the case where all \(\alpha _k \chi _k \in (0,1)\) is determined by the sign of \(\sum _k \mu _k \cot ( \chi _k \pi \alpha _k )\). In the case of two half-lines, the model fits naturally on \({{\mathbb {R}}}\) and is a version of the oscillating random walk of Kemperman. In that case, the cotangent criterion for recurrence becomes linear in \(\alpha _1\) and \(\alpha _2\); our general setting exhibits the essential nonlinearity in the cotangent criterion. For the general model, we also show existence and non-existence of polynomial moments of return times. Our moments results are sharp (and new) for several cases of the oscillating random walk; they are apparently even new for the case of a homogeneous random walk on \({{\mathbb {R}}}\) with symmetric increments of tail exponent \(\alpha \in (1,2)\).     

Asymptotic normality of quadratic forms of martingale differences

We establish the asymptotic normality of a quadratic form \(Q_n\) in martingale difference random variables \(\eta _t\) when the weight matrix A of the quadratic form has an asymptotically vanishing diagonal. Such a result has numerous potential applications in time series analysis. While for i.i.d. random variables \(\eta _t\), asymptotic normality holds under condition \(||A||_{sp}=o(||A||) \), where \(||A||_{sp}\) and ||A|| are the spectral and Euclidean norms of the matrix A, respectively, finding corresponding sufficient conditions in the case of martingale differences \(\eta _t\) has been an important open problem. We provide such sufficient conditions in this paper.     

Error Bounds for Consistent Reconstruction: Random Polytopes and Coverage Processes

Consistent reconstruction is a method for producing an estimate \(\widetilde{x} \in {\mathbb {R}}^d\) of a signal \(x\in {\mathbb {R}}^d\) if one is given a collection of \(N\) noisy linear measurements \(q_n = \langle x, \varphi _n \rangle + \epsilon _n\), \(1 \le n \le N\), that have been corrupted by i.i.d. uniform noise \(\{\epsilon _n\}_{n=1}^N\). We prove mean-squared error bounds for consistent reconstruction when the measurement vectors \(\{\varphi _n\}_{n=1}^N\subset {\mathbb {R}}^d\) are drawn independently at random from a suitable distribution on the unit-sphere \({\mathbb {S}}^{d-1}\). Our main results prove that the mean-squared error (MSE) for consistent reconstruction is of the optimal order \({\mathbb {E}}\Vert x - \widetilde{x}\Vert ^2 \le K\delta ^2/N^2\) under general conditions on the measurement vectors. We also prove refined MSE bounds when the measurement vectors are i.i.d. uniformly distributed on the unit-sphere \({\mathbb {S}}^{d-1}\) and, in particular, show that in this case, the constant \(K\) is dominated by \(d^3\), the cube of the ambient dimension. The proofs involve an analysis of random polytopes using coverage processes on the sphere.     

Large Deviations for Sums of Random Vectors Attracted to Operator Semi-Stable Laws

Let \(\{X_i, i\ge 1\}\) be i.i.d. \(\mathbb {R}^d\)-valued random vectors attracted to operator semi-stable laws and write \(S_n=\sum _{i=1}^{n}X_i\). This paper investigates precise large deviations for both the partial sums \(S_n\) and the random sums \(S_{N(t)}\), where N(t) is a counting process independent of the sequence \(\{X_i, i\ge 1\}\). In particular, we show for all unit vectors \(\theta \) the asymptotics

$$\begin{aligned} {\mathbb P}(|\langle S_n,\theta \rangle |>x)\sim n{\mathbb P}(|\langle X,\theta \rangle |>x) \end{aligned}$$

which holds uniformly for x-region \([\gamma _n, \infty )\), where \(\langle \cdot , \cdot \rangle \) is the standard inner product on \(\mathbb {R}^d\) and \(\{\gamma _n\}\) is some monotone sequence of positive numbers. As applications, the precise large deviations for random sums of real-valued random variables with regularly varying tails and \(\mathbb {R}^d\)-valued random vectors with weakly negatively associated occurrences are proposed. The obtained results improve some related classical ones.

     

A stochastic Gauss–Bonnet–Chern formula

We prove that a Gaussian ensemble of smooth random sections of a real vector bundle \(E\) over compact manifold \(M\) canonically defines a metric on \(E\) together with a connection compatible with it. Additionally, we prove a refined Gauss-Bonnet theorem stating that if the bundle \(E\) and the manifold \(M\) are oriented, then the Euler form of the above connection can be identified, as a current, with the expectation of the random current defined by the zero-locus of a random section in the above Gaussian ensemble.