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用马尔可夫转换图结构SUR模型刻画系统性风险AI 解读

We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on the basis of a novel weighted eigenvector centrality measure. An empirical application to the constituents of the S&P100 index shows that cross-firm connectivity significantly…

高维时变协方差的稀疏贝叶斯估计AI 解读

We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global–local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500…

用于推断与优化的序贯自适应贝叶斯学习算法AI 解读

The sequentially adaptive Bayesian learning algorithm (SABL) builds on and ties together ideas from sequential Monte Carlo and simulated annealing. The algorithm can be used to simulate from Bayesian posterior distributions, using either data tempering or power tempering, or for optimization. A key feature of SABL is that the introduction of information is adaptive and controlled, ensuring that the algorithm performs reliably and efficiently in a wide variety of applications…

贝叶斯压缩向量自回归AI 解读

Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller,…

利用类Copula近似进行后验重要性抽样AI 解读

We provide generic approximations to k-dimensional posterior distributions through an importance sampling strategy. The importance function is a product of k univariate of Student-t densities and a k-dimensional beta-Liouville density truncated on the hypercube. The parameters of the densities and the number of components in the mixtures are adaptively optimised along the Monte Carlo sampling. For challenging high dimensional latent Gaussian models we propose a nested…

时间序列预测中的动态贝叶斯预测综合AI 解读

We discuss model and forecast combination in time series forecasting. A foundational Bayesian perspective based on agent opinion analysis theory defines a new framework for density forecast combination, and encompasses several existing forecast pooling methods. We develop a novel class of dynamic latent factor models for time series forecast synthesis; simulation-based computation enables implementation. These models can dynamically adapt to time-varying biases,…

时变参数模型框架中的收缩方法AI 解读

Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian framework, with the aim to automatically reduce time-varying parameters to static ones, if the model is overfitting. This is achieved through placing the double gamma shrinkage prior on the process variances. An efficient Markov chain Monte Carlo scheme is developed, exploiting boosting based on the ancillarity-sufficiency interweaving strategy. The method is applicable both to TVP models for…

稀疏动态因子模型的贝叶斯估计：顺序无关与事后模态识别AI 解读

Common variation in N series is extracted into k≪N dynamic factors. We induce sparsity by using a zero point mass–normal mixture prior distribution on the loadings. Estimation and rotational identification are independent of variable ordering. Sparsity helps identifying the factor space and the factors. Rotational identification, including factor order and sign, is obtained by processing the posterior output and based on factor draws rather than factor loading draws.…

用于检测基因表达杂种优势的RNA-seq计数全贝叶斯分析AI 解读

Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. Heterosis is extensively used in agriculture, and the underlying mechanisms are unclear. To investigate the molecular basis of phenotypic heterosis, researchers search tens of thousands of genes for heterosis with respect to expression in the transcriptome. Difficulty arises in the assessment of heterosis due to composite null hypotheses and non-uniform…

通信高效的分布式统计推断AI 解读

We present a communication-efficient surrogate likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation, and Bayesian inference. For low-dimensional estimation, CSL provably improves upon naive averaging schemes and facilitates the construction of confidence intervals. For high-dimensional…

序列相关函数数据的贝叶斯半参数函数混合模型：青光眼数据应用AI 解读

Glaucoma, a leading cause of blindness, is characterized by optic nerve damage related to intraocular pressure (IOP), but its full etiology is unknown. Researchers at UAB have devised a custom device to measure scleral strain continuously around the eye under fixed levels of IOP, which here is used to assess how strain varies around the posterior pole, with IOP, and across glaucoma risk factors such as age. The hypothesis is that scleral strain decreases with age, which…

癌种特异性发病年龄外显率的贝叶斯半参数估计：Li-Fraumeni综合征应用AI 解读

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration…

高维贝叶斯向量自回归模型的后验一致性AI 解读

Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional genomics and neuroscience. These applications have also accentuated the need to investigate the behavior of the VAR model in a high-dimensional regime, which provides novel insights into the role of temporal dependence for…

通过高效数据子抽样加速 MCMCAI 解读

We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where the likelihood function for n observations is estimated from a random subset of m observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to…

社区数量未知时的概率社区检测AI 解读

A fundamental problem in network analysis is clustering the nodes into groups which share a similar connectivity pattern. Existing algorithms for community detection assume the knowledge of the number of clusters or estimate it a priori using various selection criteria and subsequently estimate the community structure. Ignoring the uncertainty in the first stage may lead to erroneous clustering, particularly when the community structure is vague. We instead propose a…

基于 Wasserstein 距离的近似贝叶斯计算AI 解读

Summary A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these data sets with the corresponding observed values. We propose to avoid the use of summaries and the ensuing loss of information by instead using the Wasserstein distance…

定性因子实验中高斯随机场的信号混杂AI 解读

Signal aliasing is an inevitable consequence of using fractional factorial designs. Unlike linear models with fixed factorial effects, for Gaussian random field models advocated in some Bayesian design and computer experiment literature, the issue of signal aliasing has not received comparable attention. In the present article, this issue is tackled for experiments with qualitative factors. The signals in a Gaussian random field can be characterized by the random effects…

We consider the statistical inverse problem of recovering a function $f:M\to \mathbb{R}$, where $M$ is a smooth compact Riemannian manifold with boundary, from measurements of general $X$-ray transforms $I_{a}(f)$ of $f$, corrupted by additive Gaussian noise. For $M$ equal to the unit disk with “flat” geometry and $a=0$ this reduces to the standard Radon transform, but our general setting allows for anisotropic media $M$ and can further model local “attenuation” effects—both…

向量自回归中关于可观测变量的先验AI 解读

Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how to translate the prior on observables into a prior on parameters using strict probability theory principles, a posterior can then be formed with standard procedures. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical…

自然利率的测度：关于暂时冲击的说明AI 解读

Summary We present evidence that the natural rate of interest is buffeted by both permanent and transitory shocks. We establish this result by estimating a benchmark model with Bayesian methods and loose priors on the unobserved drivers of the natural rate. When subject to transitory shocks, the median estimate for the US economy is more procyclical, displays a less marked secular decline, and is therefore higher following the Great Recession than most estimates in the…

利用信息丰富观测进行动态资产定价模型的贝叶斯估计AI 解读

In dynamic asset pricing models, when the model structure becomes complex and derivatives data are introduced in estimation, traditional MCMC methods converge slowly, are difficult to design efficient proposals for parameters, and have large computational cost. We propose a two-stage sequential Monte Carlo sampler based on common random numbers and a smooth particle filter. This method is robust to potential model misspecification and can deliver almost full-likelihood-based…

矩条件与贝叶斯非参数方法AI 解读

Summary Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non-parametric Bayesian set-up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a…

贝叶斯分数后验AI 解读

We consider the fractional posterior distribution that is obtained by updating a prior distribution via Bayes theorem with a fractional likelihood function, a usual likelihood function raised to a fractional power. First, we analyze the contraction property of the fractional posterior in a general misspecified framework. Our contraction results only require a prior mass condition on certain Kullback–Leibler (KL) neighborhood of the true parameter (or the KL divergence…

高维贝叶斯有向无环图模型的后验图选择与估计一致性AI 解读

Covariance estimation and selection for high-dimensional multivariate datasets is a fundamental problem in modern statistics. Gaussian directed acyclic graph (DAG) models are a popular class of models used for this purpose. Gaussian DAG models introduce sparsity in the Cholesky factor of the inverse covariance matrix, and the sparsity pattern in turn corresponds to specific conditional independence assumptions on the underlying variables. A variety of priors have been…