Noisy Hamiltonian Monte Carlo for Doubly Intractable Distributions

Hamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician’s toolbox as an alternative sampling method in settings when standard Metropolis–Hastings is inefficient. HMC generates a Markov chain on an augmented state space with transitions based on a deterministic differential flow derived from Hamiltonian mechanics. In practice, the evolution of Hamiltonian systems cannot be solved analytically, requiring numerical integration schemes. Under numerical integration, the resulting approximate solution no longer preserves the measure of the target distribution, therefore an accept–reject step is used to correct the bias. For doubly intractable distributions—such as posterior distributions based on Gibbs random fields—HMC suffers from some computational difficulties: computation of gradients in the differential flow and computation of the accept–reject proposals poses difficulty. In this article, we study the behavior of HMC when these quantities are replaced by Monte Carlo estimates. Supplemental codes for implementing methods used in the article are available online.     

一类嵌套多元指数分布相关参数的矩型估计

本文研究一类具有三个相关参数的嵌套多元指数分布,其生存函数为其中,0相似文献   

Dependence Properties of Conditional Distributions of some Copula Models

For multivariate data from an observational study, inferences of interest can include conditional probabilities or quantiles for one variable given other variables. For statistical modeling, one could fit a parametric multivariate model, such as a vine copula, to the data and then use the model-based conditional distributions for further inference. Some results are derived for properties of conditional distributions under different positive dependence assumptions for some copula-based models. The multivariate version of the stochastically increasing ordering of conditional distributions is introduced for this purpose. Results are explained in the context of multivariate Gaussian distributions, as properties for Gaussian distributions can help to understand the properties of copula extensions based on vines.     

基于Copula函数的组合资产条件相依性模型研究

条件概率分布常用来研究马尔科夫序列相依模型的构建,组合资产的相依结构受多方面的影响,资产之间的相互影响与时间上的记忆效应是组合资产两类主要的相依关系.结合条件概率的理论建立基于Copula函数相依关系模型,研究组合资产之间同期相依关系及时间上的短期相依关系,提出了模型参数的三阶段极大似然估计方法.     

Exponential Mixture Representation of Geometric Stable Distributions

We show that every strictly geometric stable (GS) random variable can be represented as a product of an exponentially distributed random variable and an independent random variable with an explicit density and distribution function. An immediate application of the representation is a straightforward simulation method of GS random variables. Our result generalizes previous representations for the special cases of Mittag-Leffler and symmetric Linnik distributions.     

On the Conditional Variance for Scale Mixtures of Normal Distributions

For a scale mixture of normal vector, X=A^1/2G, where X, G ⁿ and A is a positive variable, independent of the normal vector G, we obtain that the conditional variance covariance, Cov(X₂ X₁), is always finite a.s. for m2, where X₁ ⁿ and m<n, and remains a.s. finite even for m=1, if and only if the square root moment of the scale factor is finite. It is shown that the variance is not degenerate as in the Gaussian case, but depends upon a function S_A, m(·) for which various properties are derived. Application to a uniform and stable scale of normal distributions are also given.     

Dependence Properties of Multivariate Mixture Distributions and Their Applications

Consider a multivariate mixture model where the random variables X ₁, ..., X _n given (₁, ..., _n ), are conditionally independent. Conditions are obtained under which different kinds of positive dependence hold among X _i 's. The results obtained are applied to a variety of problems including the concomitants of order statistics and of record values; and to frailty models.     

一个新的指数削减椭圆形分布族

A new family of univariate exponential slash distribution is introduced, which is based on elliptical distributions and defined by means of a stochastic representation as the scale mixture of an elliptically distributed random variable with respect to the power of an exponential random variable. The same idea is extended to the multivariate case. General properties of the resulting families, including their moments and kurtosis coefficient, are studied. And inferences based on methods of moment and maximum likelihood are discussed. A real data is presented to show this family is flexible and fits much better than other related families.     

Bayesian Inference on a Mixture Model With Spatial Dependence

We introduce a new technique to select the number of components of a mixture model with spatial dependence. The method consists of an estimation of the integrated completed likelihood based on a Laplace’s approximation and a new technique to deal with the normalizing constant intractability of the hidden Potts model. Our proposal is applied to a real satellite image. Supplementary materials are available online.     

Markov approximation of homogeneous lattice random fields

We refine some well-known approximation theorems in the theory of homogeneous lattice random fields. In particular, we prove that every translation invariant Borel probability measure on the space X of finite-alphabet configurations on ^d, d1, can be weakly approximated by Markov measures _n with supp(_n)=X and with the entropies h(_n)h(). The proof is based on some facts of Thermodynamic Formalism; we also present an elementary constructive proof of a weaker version of this theorem.Mathematics Subject Classifications (2000): Primary 28D20, 37C85, 60G60; secondary 82B20Dedicated to Professor A. I. Vorobyov, member of the Russian Academy of Sciences and Director of the Hematology Research Center of the Russian Academy of Medical Sciences, on the occasion of his 75th birthday     

用于指数可靠性增长模型的一类新的先验分布

该文提出了可用于指数分布产品四种可靠性增长试验方案的一类新的先验分布. 这类先验分布以条件分布形式给出, 它适合可靠性增长试验中的各种情况. 各阶段的条件均值和条件方差的表达式被获得, 先验分布的形式与它们的参数间的关系被讨论. 这些结果有助于与专家意见相结合.本文还给出试验末尾产品可靠性的后验密度, Bayesian估计和Bayesian下限.     

向量组线性相关性的教学方法与技巧

向量组线性相关性是线性代数教学中的一项重要内容．由于概念比较抽象、定理难以理解,因此一直是线性代数教学环节中的一项难点．通过对向量组线性相关性的定义以及判断方法进行了形象的描述,建立向量组线性相关性与矩阵、线性方程组之间的关系,有利于学生理解向量组线性相关性的真正内涵与简便求解方法．     

Modelling Dependence Uncertainty in the Extremes of Markov Chains

General theory on the extremes of stationary processes leads only to a limited representation for extreme-state behaviour, usually summarised by the extremal index. In practice this means that other quantities such as the duration of extreme episodes or aggregate of threshold exceedances within a cluster require stronger model assumptions. In this paper we propose a model based on a Markov assumption for the underlying process, with high-level transitions determined by an asymptotically motivated distribution. This idea is not new: Smith et al. (1997) first developed the statistical basis for such a procedure, which was subsequently extended by Bortot and Tawn (1998) to better handle the case of weak extremal temporal dependence for which the extremal index is unity. We adopt similar procedures to each of these earlier works, but suggest a different model for the Markov transitions. The model we use was developed by Coles and Pauli (2002) to enable a Bayesian inference of multivariate extremes that provides a posterior distribution on the status of asymptotic independence. By adopting this model in the Markov framework, we show here that the model has all the flexibility of the model developed by Bortot and Tawn (1998), but with the additional advantage of providing a posterior probability on the extremal index and inferences that take full account of the uncertainty in the extremal index. We demonstrate the methodology on both simulated data and a time series of daily rainfall that exhibit weak temporal dependence at extreme levels.     

A scalable pairwise class interaction framework for multidimensional classification

We present a general framework for multidimensional classification that captures the pairwise interactions between class variables. The pairwise class interactions are encoded using a collection of base classifiers (Phase 1), for which the class predictions are combined in a Markov random field that is subsequently used for multidimensional inference (Phase 2); thus, the framework can be positioned between multilabel Bayesian classifiers and label transformation-based approaches. Our proposal leads to a general framework supporting a wide range of base classifiers in the first phase as well as different inference methods in the second phase. We describe the basic framework and its main properties, as well as strategies for ensuring the scalability of the framework. We include a detailed experimental evaluation based on a range of publicly available databases. Here we analyze the overall performance of the framework and we test the behavior of the different scalability strategies proposed. A comparison with other state-of-the-art multidimensional classifiers show that the proposed framework either outperforms or is competitive with the tested straw-men methods.     

Reconstructibility of a general DNA evolution model

In this paper, we analyze the tree reconstruction problem, to identify whether there is non-vanishing information of the root, as the level of the tree goes to infinity. Although it has been studied in numerous contexts, the existing literature with rigorous reconstruction thresholds established are very limited, and it becomes extremely challenging when the model under investigation has 4 states, one of whose interpretations is the four main bases found in Deoxyribonucleic acid (DNA) and Ribonucleic acid (RNA): guanine [G], cytosine [C], adenine [A], and thymine [T]. In this paper, we study a general DNA evolution model, which distinguishes between transitions and transversions, and allow transversions to occur at the same rate but that rate can be different from the rates for transitions. The sufficient condition for reconstruction is rigorously established.     

次贷危机下中国股市与国外股市相依性分析——基于Markov机制转换模型

文章通过选取2004年1月1日到2009年6月30日中国、香港、日本、英国和澳大利亚五个股票市场日收盘价的道琼斯数据,采用三状态Markov机制转换模型研究这些股市间相依性结构的变化。通过对目标股市结构变化的研究,可以描述并预测股市的波动性,从而指导风险管理。实证分析表明,在有机制转换条件下,澳大利亚与英国、日本股市间的相依性比无机制转换条件下均有所下降,而中国与香港股市间相依性却大幅上升。同时,本文采用总体拟合效果法来选取合适的copula函数并运用基于copula理论的相关系数法进行对比研究,发现次贷危机后各股市间的尾部相依性出现不同的变化,市场收益率呈现下降趋势,波动性均有所增加。其中,澳大利亚与英国股市间的尾部相依性最强,而中国股市与其他股市之间的相依性较弱,说明受到影响的程度较小。     

Characterizing and Generating Bivariate Empirical Rank Distributions Satisfying Certain Positive Dependence Concepts

Abstract

This article introduces an approach for characterizing the classes of empirical distributions that satisfy certain positive dependence notions. Mathematically, this can be expressed as studying certain subsets of the class S_N of permutations of 1, …, N, where each subset corresponds to some positive dependence notions. Explicit techniques for it-eratively characterizing subsets of S_N that satisfy certain positive dependence concepts are obtained and various counting formulas are given. Based on these techniques, graph-theoretic methods are used to introduce new and more efficient algorithms for constructively generating and enumerating the elements of various of these subsets of S_N. For example, the class of positively quadrant dependent permutations in S_N is characterized in this fashion.     

Density Deconvolution of Different Conditional Distributions

Recently, a new technique to circumvent the ill-posedness of the deconvolution problem has been suggested. This technique is based on what is known as multi-channel convolution system. In this paper, we modify and develop this technique in order to adapt it for statistical use. We then apply it to the problem of estimation of deconvolution density in the case of different conditional densities. This method enables us to combine equations efficiently for any set of conditional densities and to construct estimators in cases where the characteristic functions of the conditional distributions vanish at some points, as it happens in the case of uniform and triangular distributions.     

人民币汇率市场化,结构相依与结构突变

运用能够识别资本市场结构突变与区制变化的Markov区制转换模型,基于非线性相依结构研究中的藤Copula分析框架,文章以考察人民币汇率市场化进程中的结构相依与突变特征为切入点,重点研究两次汇改以及金融危机时期人民币汇率在四个阶段的结构转换及非对称动态相依特征。文章采用GJR-GARCH模型探讨人民币汇率市场的"杠杆效应"。在此基础上,文章对两次汇改以及美国次贷危机时期人民币汇率市场的结构突变和区制转换的进行识别。研究发现,Markov区制转换模型能够准确地捕捉到人民币汇率第一次汇改的临界点,但在捕捉第二次汇改临界点方面却存在一定的滞后反应。并且,该模型对美联储采取第一轮量化宽松的货币政策的捕获,也表现出较好的能力。进一步地,文章运用藤Copula分析框架探讨了不同人民币汇率市场之间的非线性相依结构。研究表明,整体而言,采用t-Copula的藤结构在捕捉人民币汇市之间的相依结构方面表现出良好的刻画效果。