Bivariate Random Vectors Research Articles

A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval

This paper presents a texture descriptor based on wavelet frame transforms. At each position in the image, and for each resolution level, we consider both vertical and horizontal wavelet detail coefficients as the components of a bivariate random vector. The magnitudes and angles of these vectors are computed. At each level the empirical histogram of magnitudes is modeled by a Generalized Gamma distribution, and the empirical histogram of angles is modeled by a different version of the von Mises distribution that accounts for histograms with 2 modes. Each texture is characterized by few parameters. A new distance is presented (based on the Kullback–Leibler divergence) that allows giving relative importance to each model and to each resolution level. This distance is later conveniently adapted to provide for rotation invariance, by establishing equivalence classes over distributions of angles. Through a broad set of experiments on three different image databases, we demonstrate that our new descriptor and distance measure can be successfully applied in the context of texture retrieval. We compare our system to several relevant methods in this field in terms of retrieval performance and number of parameters used by each method. We also include some classification tests. In all the tests, we obtain superior retrieval rates for a set of fewer parameters involved.

On conditional extreme values of random vectors with polar representation

An effective approach for studying the asymptotics of bivariate random vectors is to search for the limits of conditional probabilities where the conditioning variable becomes large. In this context, elliptical and related distributions have been extensively investigated. A quite general model was presented by Fougeres and Soulier (Limit conditional distributions for bivariate vectors with polar representation in Stochastic Models, 2010), who derived a conditional limit theorem for random vectors (X, Y) with a polar representation R · (u(T), v(T)), where R, T are stochastically independent and R is in the Gumbel max-domain of attraction. We reformulate their assumptions, such that they have a simpler structure, display more clearly the geometry of the curves (u(t), v(t)) and allow us to deduce interesting generalizations into two directions: u has several global maxima instead of only one, the curve (u(t), v(t)) is no longer differentiable, but forms a “cusp”. The latter generalization yields results where only random norming leads to a non-degenerate limit statement. Ideas and results are elucidated by several figures.

Strength of tail dependence based on conditional tail expectation

We use the conditional distribution and conditional expectation of one random variable given the other one being large to capture the strength of dependence in the tails of a bivariate random vector. We study the tail behavior of the boundary conditional cumulative distribution function (cdf) and two forms of conditional tail expectation (CTE) for various bivariate copula families. In general, for nonnegative dependence, there are three levels of strength of dependence in the tails according to the tail behavior of CTEs: asymptotically linear, sub-linear and constant. For each of these three levels, we investigate the tail behavior of CTEs for the marginal distributions belonging to maximum domain of attraction of Fréchet and Gumbel, respectively, and for copula families with different tail behavior.

Expansions and penultimate distributions of maxima of bivariate normal random vectors

It is well known that the marginal maxima of n standard normal random vectors with correlation coefficient ρ<1 are asymptotically independent. In this article, the residual dependence will be captured by asymptotic expansions and certain penultimate distributions including the case where ρ(n)↑1 at a certain rate.

Information dependency: Strong consistency of Darbellay–Vajda partition estimators

The Darbellay–Vajda partition scheme is a well known method to estimate the information dependency. This estimator belongs to a class of data-dependent partition estimators. We would like to prove that with some simple conditions, the Darbellay–Vajda partition estimator is a strong consistency for the information dependency estimation of a bivariate random vector. This result is an extension of Silva and Narayanan's (2010a,b) work which gives some simple conditions to confirm that the Gessaman's partition estimator and the tree-quantization partition estimator, other estimators in the class of data-dependent partition estimators, are strongly consistent.

A note on “Maximum distributions for [formula omitted]-symmetric vectors are skewed [formula omitted]-symmetric distributions” by

In a recent paper, Batún-Cutz et al. (2013) showed that the density of the maximum of the components of a l2,p-symmetrically distributed random vector have skewed l1,p-symmetric distribution. Their proof, based on a geometric measure representation for the distribution of the maximum, is quite involved and long. Here, we present a very simple proof of their main result by using a property of the distribution of the maximum of an exchangeable bivariate random vector.

C-NORTA: A Rejection Procedure for Sampling from the Tail of Bivariate NORTA Distributions

We propose C-NORTA, an exact algorithm to generate random variates from the tail of a bivariate NORTA random vector. (A NORTA random vector is specified by a pair of marginals and a rank or product–moment correlation, and it is sampled using the popular NORmal-To-Anything procedure.) We first demonstrate that a rejection-based adaptation of NORTA on such constrained random vector generation problems may often be fundamentally intractable. We then develop the C-NORTA algorithm, relying on strategic conditioning of the NORTA vector, followed by efficient approximation and acceptance/rejection steps. We show that, in a certain precise asymptotic sense, the sampling efficiency of C-NORTA is exponentially larger than what is achievable through a naïve application of NORTA. Furthermore, for at least a certain class of problems, we show that the acceptance probability within C-NORTA decays only linearly with respect to a defined rarity parameter. The corresponding decay rate achievable through a naïve adaptation of NORTA is exponential. We provide directives for efficient implementation.

Bivariate gamma-geometric law and its induced Lévy process

In this article we introduce a three-parameter extension of the bivariate exponential-geometric (BEG) law (Kozubowski and Panorska, 2005) [4]. We refer to this new distribution as the bivariate gamma-geometric (BGG) law. A bivariate random vector (X,N) follows the BGG law if N has geometric distribution and X may be represented (in law) as a sum of N independent and identically distributed gamma variables, where these variables are independent of N. Statistical properties such as moment generation and characteristic functions, moments and a variance–covariance matrix are provided. The marginal and conditional laws are also studied. We show that BBG distribution is infinitely divisible, just as the BEG model is. Further, we provide alternative representations for the BGG distribution and show that it enjoys a geometric stability property. Maximum likelihood estimation and inference are discussed and a reparametrization is proposed in order to obtain orthogonality of the parameters. We present an application to a real data set where our model provides a better fit than the BEG model. Our bivariate distribution induces a bivariate Lévy process with correlated gamma and negative binomial processes, which extends the bivariate Lévy motion proposed by Kozubowski et al. (2008) [6]. The marginals of our Lévy motion are a mixture of gamma and negative binomial processes and we named it BMixGNB motion. Basic properties such as stochastic self-similarity and the covariance matrix of the process are presented. The bivariate distribution at fixed time of our BMixGNB process is also studied and some results are derived, including a discussion about maximum likelihood estimation and inference.

Measures of radial asymmetry for bivariate random vectors

This article considers the problem of measuring radial asymmetry of a bivariate vector of continuous random variables whose components are symmetric about the center of symmetry. We introduce a set of axioms for measures of radial asymmetry, define new measures satisfying the axiomatic, study some properties, and present corresponding results for sample statistics. Several examples illustrate our results.

Bivariate symbolic regression models for interval-valued variables

Interval-valued variables have become very common in data analysis. Up until now, symbolic regression mostly approaches this type of data from an optimization point of view, considering neither the probabilistic aspects of the models nor the nonlinear relationships between the interval response and the interval predictors. In this article, we formulate interval-valued variables as bivariate random vectors and introduce the bivariate symbolic regression model based on the generalized linear models theory which provides much-needed exibility in practice. Important inferential aspects are investigated. Applications to synthetic and real data illustrate the usefulness of the proposed approach.

Supermigrative copulas and positive dependence

Recent investigations about notions of bivariate aging have underlined the need to introduce some new properties of positive dependence for a bivariate random vector. Here, by using the recent notion of supermigrativity of a bivariate copula, a positive dependence property is introduced and investigated. Comparisons with other notions of positive dependence are also presented.

Representation of Downton’s bivariate exponential random vector and its applications

Downton’s bivariate exponential distribution is one of the most important bivariate distributions in reliability theory. In this paper a simple representation for Downton’s bivariate exponential random vector is given. As an application of this representation, we consider a reliability model where an item is subject to shocks and obtain an explicit expression for the long-run cost rate.

Improved Fréchet Bounds and Model-Free Pricing of Multi-Asset Options

Improved bounds on the copula of a bivariate random vector are computed when partial information is available, such as the values of the copula on a given subset of [0, 1]2, or the value of a functional of the copula, monotone with respect to the concordance order. These results are then used to compute model-free bounds on the prices of two-asset options which make use of extra information about the dependence structure, such as the price of another two-asset option.

Improved Fréchet Bounds and Model-Free Pricing of Multi-Asset Options

Improved bounds on the copula of a bivariate random vector are computed when partial information is available, such as the values of the copula on a given subset of [0, 1]2, or the value of a functional of the copula, monotone with respect to the concordance order. These results are then used to compute model-free bounds on the prices of two-asset options which make use of extra information about the dependence structure, such as the price of another two-asset option.

Exact tail asymptotics in bivariate scale mixture models

Let (X, Y) = (RU 1, RU 2) be a given bivariate scale mixture random vector, with R > 0 independent of the bivariate random vector (U 1, U 2). In this paper we derive exact asymptotic expansions of the joint survivor probability of (X, Y) assuming that R has distribution function in the Gumbel max-domain of attraction, and (U 1, U 2) has a specific local asymptotic behaviour around some absorbing point. We apply our results to investigate the asymptotic behaviour of joint conditional excess distribution and the asymptotic independence for two models of bivariate scale mixture distributions.

A nonparametric symmetry test for absolutely continuous bivariate copulas

Based on the works by Klement and Mesiar (Comment Math Univ Carolinae 47:141–148, 2006) and Nelsen (Stat Pap 48:329–336, 2007) on maximal asymmetry of copulas, we define and study the concept of tri-symmetry and we propose a simple statistic to test symmetry of a bivariate copula, given a random sample of an absolutely continuous bivariate random vector. We also make a power comparison against some other well known nonparametric symmetry tests.

Estimation of conditional laws given an extreme component

Let $(X,Y)$ be a bivariate random vector. The estimation of a probability of the form $P(Y\leq y \mid X >t) $ is challenging when $t$ is large, and a fruitful approach consists in studying, if it exists, the limiting conditional distribution of the random vector $(X,Y)$, suitably normalized, given that $X$ is large. There already exists a wide literature on bivariate models for which this limiting distribution exists. In this paper, a statistical analysis of this problem is done. Estimators of the limiting distribution (which is assumed to exist) and the normalizing functions are provided, as well as an estimator of the conditional quantile function when the conditioning event is extreme. Consistency of the estimators is proved and a functional central limit theorem for the estimator of the limiting distribution is obtained. The small sample behavior of the estimator of the conditional quantile function is illustrated through simulations.

Limit Conditional Distributions for Bivariate Vectors with Polar Representation

We investigate conditions for the existence of the limiting conditional distribution of a bivariate random vector when one component becomes large. We revisit the existing literature on the topic, and present some new sufficient conditions. We concentrate on the case where the conditioning variable belongs to the maximum domain of attraction of the Gumbel law, and we study geometric conditions on the joint distribution of the vector. We show that these conditions are of a local nature and imply asymptotic independence when both variables belong to the domain of attraction of an extreme value distribution. The new model we introduce can also be useful to simulate bivariate random vectors with a given limiting conditional distribution.

On the rate of convergence to bivariate stable laws

In the paper, we approximate the distribution function of a sum of independent nonidentically distributed bivariate random vectors by the distribution function of a stable vector and estimate the accuracy of such an approximation. The obtained general result is only a little bit worse when compared with known estimates for the case of multivariate independent and identically distributed random vectors or univariate nonidentically distributed summands. We also apply the result obtained to a specific scheme arising when considering the so-called Increment-Ratio Statistics.

On the cumulative quantile regression process

Let (X, Y) be a bivariate random vector and F(x) the marginal distribution function of X. The quantile regression (QR) function of Y on X is defined as r(u) = E[Y | F(X) = u] and the cumulative QR function (CQR) M(u) as its integral over [0, u]. The empirical counterpart based on a sample of size n is M n (u). In this paper, we construct strong Gaussian approximations of the associated CQR process under appropriate assumptions. The construction provides a firm basis for the study of functional statistics based on M in (u). A law of the iterated logarithm for the CQR process follows from our result.