arXiv subjects
Publications and source records attributed to Gerard Letac.
We construct a family of matrix ensembles that fits Anshelevich's regression postulates for "Meixner laws on matrices". We show that the Laplace transform of a general n by n Meixner matrix ensemble satisfies a system of partial differential equations which is explicitly solvable for n=2. We rely on these solutions to identify the six types of 2 by 2 Meixner matrix ensembles.
Let $P$ a probability on the real line generating a natural exponential family $(P_t)_{t\in \R}$. We show that $t$ is a median of $P_t$ for all $t$ only if $P$ is the standard Gaussian law $N(0.1).$ The proof is based on the Choquet Deny equation.
Consider $G=SL_2(\mathbb{Z})/\{\pm I\}$ acting on the complex upper half plane $H$ by $h_M(z)=\frac{az+b}{cz+d},$ for $M \in G$. Let $D=\{z \in H: |z|\geq 1, |\Re(z)|\leq 1/2\}$. We consider the set $\mathcal{E} \subset G$ with the $9$ elements $M$, different from the identity, such that $(MM^T)\leq 3$. We equip the tiling of $H$ defined by $\mathbb{D}=\{h_M(D), M \in G\}$ with a graph structure where the neighbours are defined by $h_M(D) \cap h_{M'}(D) \neq \emptyset$, equivalently $M^{-1}M' \in \mathcal{E}$. The present paper studies several Markov chains related to the above structure. We show that the simple random walk on the above graph converges a.s. to a point $X$ of the real line with the same distribution of $S_2 W^{S_1}$, where $S_1,S_2,W$ are independent with $\Pr (S_i=\pm 1)=1/2$ and where $W$ is valued in $(0,1)$ with distribution $\Pr(W<w)=?(w)$. Here $?$ is the Minkowski function. If $K_1, K_2, \ldots$ are i.i.d with distribution $\Pr (K_i=n)= 1/2^n$ for $n=1,2,\ldots$, then $W= \frac{1}{K_1+\frac {1}{K_2+\ldots}}$: this known result (Isola (2014)) is derived again here.
Gaussian graphical models are relevant tools to learn conditional independence structure between variables. In this class of models, Bayesian structure learning is often done by search algorithms over the graph space. The conjugate prior for the precision matrix satisfying graphical constraints is the well-known G-Wishart. With this prior, the transition probabilities in the search algorithms necessitate evaluating the ratios of the prior normalizing constants of G-Wishart. In moderate to high-dimensions, this ratio is often approximated using sampling-based methods as computationally expensive updates in the search algorithm. Calculating this ratio so far has been a major computational bottleneck. We overcome this issue by representing a search algorithm in which the ratio of normalizing constant is carried out by an explicit closed-form approximation. Using this approximation within our search algorithm yields significant improvement in the scalability of structure learning without sacrificing structure learning accuracy. We study the conditions under which the approximation is valid. We also evaluate the efficacy of our method with simulation studies. We show that the new search algorithm with our approximation outperforms state-of-the-art methods in both computational efficiency and accuracy. The implementation of our work is available in the R package BDgraph.
If $α$ is a probability on $\mathbb{R}^d$ and $t>0,$ consider the Dirichlet random probability $P_t\sim\mathcal{D}(tα) ;$ it is such that for any measurable partition $(A_0,\ldots,A_k)$ of $\mathbb{R}^d$ then $(P_t(A_0),\ldots,P_t(A_k))$ is Dirichlet distributed with parameters $(tα(A_0)\ldots,tα(A_k)).$ If $\int_{\mathbb{R}^d}\log(1+\|x\|)α(dx)<\infty$ the random variable $\int_{\mathbb{R}^d}xP_t(dx)$ of $\mathbb{R}^d$ does exist and we denote by $μ(tα)$ its distribution. The Dirichlet curve associated to the probability $α$ is the map $t\mapsto μ(tα).$ It has simple properties like $\lim_{t\searrow 0}μ(tα)=α$ and $\lim_{t\rightarrow \infty}μ(tα)=δ_m$ when $m=\int_{\mathbb{R}^d} xα(dx)$ exists. The present paper shows first that if $m$ exists and if $ψ$ is a convex function on $\mathbb{R}^d$ then $t\mapsto \int_{\mathbb{R}^d}ψ(x)μ(tα)(dx)$ is a decreasing function, which means that $t\mapsto μ(tα)$ is decreasing according to the Strassen convex order of probabilities. The second aim of the paper is to prove a group of results around the following question: if $μ(tα)=μ(sα)$ for some $0\leq s<t$, can we claim that $μ$ is Cauchy distributed in $\mathbb{R}^d?$
This article provides tools for the study of the Dirichlet random walk in $\mathbb{R}^d$. By this we mean the random variable $W=X_1Θ_1+\cdots+X_nΘ_n$ where $X=(X_1,\ldots,X_n) \sim \mathcal{D}(q_1,\ldots,q_n)$ is Dirichlet distributed and where $Θ_1,\ldots Θ_n$ are iid, uniformly distributed on the unit sphere of $\mathbb{R}^d$ and independent of $X.$ In particular we compute explicitely in a number of cases the distribution of $W.$ Some of our results appear already in the literature, in particular in the papers by Gérard Le Caër (2010, 2011). In these cases, our proofs are much simpler from the original ones, since we use a kind of Stieltjes transform of $W$ instead of the Laplace transform: as a consequence the hypergeometric functions replace the Bessel functions. A crucial ingredient is a particular case of the classical and non trivial identity, true for $0\leq u\leq 1/2$:$$_2F_1(2a,2b;a+b+\frac{1}{2};u)= \_2F_1(a,b;a+b+\frac{1}{2};4u-4u^2).$$ We extend these results to a study of the limits of the Dirichlet random walks when the number of added terms goes to infinity, interpreting the results in terms of an integral by a Dirichlet process. We introduce the ideas of Dirichlet semigroups and of Dirichlet infinite divisibility and characterize these infinite divisible distributions in the sense of Dirichlet when they are concentrated on the unit ball of $\mathbb{R}^d.$ {4mm}\noindent \textsc{Keywords:} Dirichlet processes, Stieltjes transforms, random flight, distributions in a ball, hyperuniformity, infinite divisibility in the sense of Dirichlet. {4mm}\noindent \textsc{AMS classification}: 60D99, 60F99.
Let $X$, $B$ and $Y$ be three Dirichlet, Bernoulli and beta independent random variables such that $X\sim \mathcal{D}(a_0,...,a_d),$ such that $\Pr(B=(0,...,0,1,0,...,0))=a_i/a$ with $a=\sum_{i=0}^da_i$ and such that $Y\sim β(1,a).$ We prove that $X\sim X(1-Y)+BY.$ This gives the stationary distribution of a simple Markov chain on a tetrahedron. We also extend this result to the case when $B$ follows a quasi Bernoulli distribution $\mathcal{B}_k(a_0,...,a_d)$ on the tetrahedron and when $Y\sim β(k,a)$. We extend it even more generally to the case where $X$ is a Dirichlet process and $B$ is a quasi Bernoulli random probability. Finally the case where the integer $k$ is replaced by a positive number $c$ is considered when $a_0=...=a_d=1.$ \textsc{Keywords} \textit{Perpetuities, Dirichlet process, Ewens distribution, quasi Bernoulli laws, probabilities on a tetrahedron, $T_c$ transform, stationary distribution.} AMS classification 60J05, 60E99.
A standard tool for model selection in a Bayesian framework is the Bayes factor which compares the marginal likelihood of the data under two given different models. In this paper, we consider the class of hierarchical loglinear models for discrete data given under the form of a contingency table with multinomial sampling. We assume that the Diaconis-Ylvisaker conjugate prior is the prior distribution on the loglinear parameters and the uniform is the prior distribution on the space of models. Under these conditions, the Bayes factor between two models is a function of their prior and posterior normalizing constants. These constants are functions of the hyperparameters $(m,α)$ which can be interpreted respectively as marginal counts and the total count of a fictive contingency table. We study the behaviour of the Bayes factor when $α$ tends to zero. In this study two mathematical objects play a most important role. They are, first, the interior $C$ of the convex hull $\bar{C}$ of the support of the multinomial distribution for a given hierarchical loglinear model together with its faces and second, the characteristic function $\mathbb{J}_C$ of this convex set $C$. We show that, when $α$ tends to 0, if the data lies on a face $F_i$ of $\bar{C_i},i=1,2$ of dimension $k_i$, the Bayes factor behaves like $α^{k_1-k_2}$. This implies in particular that when the data is in $C_1$ and in $C_2$, i.e. when $k_i$ equals the dimension of model $J_i$, the sparser model is favored, thus confirming the idea of Bayesian regularization.
Consider an exponential dispersion model (EDM) generated by a probability $ μ$ on $[0,\infty )$ which is infinitely divisible with an unbounded Lévy measure $ν$. The Jorgensen set (i.e., the dispersion parameter space) is then $\mathbb{R}^{+}$, in which case the EDM is characterized by two parameters: $θ_{0}$ the natural parameter of the associated natural exponential family and the Jorgensen (or dispersion) parameter $t$. Denote by $EDM(θ_{0},t)$ the corresponding distribution and let $Y_{t}$ is a r.v. with distribution $EDM(θ_0,t)$. Then if $ν((x,\infty ))\sim -\ell \log x$ around zero we prove that the limiting law $F_0$ of $ Y_{t}^{-t}$ as $t\rightarrow 0$ is of a Pareto type (not depending on $ θ_0$) with the form $F_0(u)=0$ for $u<1$ and $1-u^{-\ell }$ for $ u\geq 1$. Such a result enables an approximation of the distribution of $ Y_{t}$ for relatively small values of the dispersion parameter of the corresponding EDM. Illustrative examples are provided.
Hazard rates play an important role in various areas, e.g., reliability theory, survival analysis, biostatistics, queueing theory and actuarial studies. Mixtures of distributions are also of a great preeminence in such areas as most populations of components are indeed heterogeneous. In this study we present a sufficient condition for mixtures of two elements\ of the same natural exponential family (NEF) to have an increasing hazard rate. We then apply this condition to some classical NEF's having either quadratic, or cubic variance functions (VF) and others as well. A particular attention is devoted to the hyperbolic cosine NEF having a quadratic VF, the Ressel NEF having a cubic VF and to the Kummer distributions of type 2 NEF. The application of such a sufficient condition is quite intricate and cumbersome, in particular when applied to the latter three NEF's. Various lemmas and propositions are needed then to verify this condition for these NEF's.
Given an arbitrary three-dimensional correlation matrix, we prove that there exists a three-dimensional joint distribution for the random variable $(X,Y,Z)$ such that $X$,$Y$ and $Z$ are identically distributed with beta distribution $β_{k,k}(dx)$ on $(0,1)$ if $k\geq 1/2$. This implies that any correlation structure can be attained for three-dimensional copulas.
If the space $\mathcal{Q}$ of quadratic forms in $\mathbb{R}^n$ is splitted in a direct sum $\mathcal{Q}_1\oplus...\oplus \mathcal{Q}_k$ and if $X$ and $Y$ are independent random variables of $\mathbb{R}^n$, assume that there exist a real number $a$ such that $E(X|X+Y)=a(X+Y)$ and real distinct numbers $b_1,...,b_k$ such that $E(q(X)|X+Y)=b_iq(X+Y)$ for any $q$ in $\mathcal{Q}_i.$ We prove that this happens only when $k=2$, when $\mathbb{R}^n$ can be structured in a Euclidean Jordan algebra and when $X$ and $Y$ have Wishart distributions corresponding to this structure.