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基于MCMC输出的模型设定检验AI 解读

Two test statistics are proposed to determine model specification after a model is estimated by an MCMC method. The first test is the MCMC version of IOSA test and its asymptotic null distribution is normal. The second test is motivated from the power enhancement technique of Fan et al. (2015). It combines a component (J1) that tests a null point hypothesis in an expanded model and a power enhancement component (J0) obtained from the first test. It is shown that J0 converges…

线性双重自回归AI 解读

This paper proposes the linear double autoregression, a conditional heteroscedastic model with a conditional mean structure but compatible with the quantile regression. The existence of a strictly stationary solution is discussed, for which a necessary and sufficient condition is established. A doubly weighted quantile regression estimation procedure is introduced, where the first set of weights ensures the asymptotic normality of the estimator and the second set improves…

需求系统的灵活估计：Copula 方法AI 解读

Summary In this paper we study the own‐price elasticity for gasoline in demand systems involving three expenditure categories in the transportation sector in Canada: gasoline, local transportation, and intercity transportation for Canadian households from 1997 to 2009. In particular, we conduct a replication of Chang and Serletis, (The demand for gasoline: Evidence from household survey data, Journal of Applied Econometrics , 2014, 29 , 291–343) hereafter CS, who—using TSP…

广义线性模型中的 g 先验混合AI 解读

Mixtures of Zellner’s g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to generalized linear models (GLMs) have been proposed in the literature; however, the choice of prior distribution of g and resulting properties for inference have received considerably less attention. In this article, we unify mixtures of g-priors in GLMs…

复值功能磁共振成像脑激活检测的贝叶斯变量选择方法AI 解读

Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small…

高维均值相等性的高功效贝叶斯检验AI 解读

We develop a Bayes factor-based testing procedure for comparing two population means in high-dimensional settings. In ‘large-p-small-n” settings, Bayes factors based on proper priors require eliciting a large and complex p × p covariance matrix, whereas Bayes factors based on Jeffrey’s prior suffer the same impediment as the classical Hotelling T2 test statistic as they involve inversion of ill-formed sample covariance matrices. To circumvent this limitation, we propose that…

利用状态区段的可逆跳跃 MCMC 发现恶意软件家族AI 解读

Malware is computer software that has either been designed or modified with malicious intent. Hundreds of thousands of new malware threats appear on the internet each day. This is made possible through reuse of known exploits in computer systems that have not been fully eradicated; existing pieces of malware can be trivially modified and combined to create new malware, which is unknown to anti-virus programs. Finding new software with similarities to known malware is…

实时追踪媒体对选民选择的影响：贝叶斯动态联合模型AI 解读

Commonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents’ exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications, such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor…

生物膜成像大型高斯模型的多项式加速求解：理论与有限精度AI 解读

Three dimensional confocal scanning laser microscope images offer dramatic visualizations of the action of living biofilms before and after interventions. Here we use confocal microscopy to study the effect of a treatment over time that causes a biofilm to swell and contract due to osmotic pressure changes. From these data, our goal is to reconstruct biofilm surfaces, to estimate the effect of the treatment on the biofilm's volume, and to quantify the related uncertainties.…

贝叶斯半参数混合效应马尔可夫模型及发声语法应用AI 解读

Studying the neurological, genetic, and evolutionary basis of human vocal communication mechanisms using animal vocalization models is an important field of neuroscience. The datasets typically comprise structured sequences of syllables or “songs” produced by animals from different genotypes under different social contexts. It has been difficult to come up with sophisticated statistical methods that appropriately model animal vocal communication syntax. We address this need…

超越正态假设的可处理贝叶斯变量选择AI 解读

Bayesian variable selection often assumes normality, but the effects of model misspecification are not sufficiently understood. There are sound reasons behind this assumption, particularly for large p: ease of interpretation, analytical, and computational convenience. More flexible frameworks exist, including semi- or nonparametric models, often at the cost of some tractability. We propose a simple extension that allows for skewness and thicker-than-normal tails but…

矩条件模型的贝叶斯估计与比较AI 解读

In this article, we develop a Bayesian semiparametric analysis of moment condition models by casting the problem within the exponentially tilted empirical likelihood (ETEL) framework. We use this framework to develop a fully Bayesian analysis of correctly and misspecified moment condition models. We show that even under misspecification, the Bayesian ETEL posterior distribution satisfies the Bernstein–von Mises (BvM) theorem. We also develop a unified approach based on…

带变量选择的贝叶斯近似核回归AI 解读

Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this paper, we propose a novel framework that provides an effect size analog for each explanatory variable in Bayesian kernel regression models when…

用于变量选择的粒子 EM 算法AI 解读

Despite its long history of success, the EM algorithm has been vulnerable to local entrapment when the posterior/likelihood is multi-modal. This is particularly pronounced in spike-and-slab posterior distributions for Bayesian variable selection. The main thrust of this article is to introduce the particle EM algorithm, a new population-based optimization strategy that harvests multiple modes in search spaces that present many local maxima. Motivated by nonparametric…

统一贝叶斯个案影响分析中的曲率与重要性抽样方法AI 解读

Methods for summarizing case influence in Bayesian models take essentially two forms: (1) use common divergence measures for calculating distances between the full-data posterior and the case-deleted posterior, and (2) measure the impact of infinitesimal perturbations to the likelihood to study local case influence. Methods based on approach (1) lead naturally to considering the behavior of case-deletion importance sampling weights (the weights used to approximate samples…

贝叶斯渐近理论的频率学派方法AI 解读

Ergodic theorem shows that ergodic averages of the posterior draws converge in probability to the posterior mean under the stationarity assumption. The literature also shows that the posterior distribution is asymptotically normal when the sample size of the original data considered goes to infinity. To the best of our knowledge, there is little discussion on the large sample behaviour of the posterior mean. In this paper, we aim to fill this gap. In particular, we extend…

重复博弈策略的非参数贝叶斯推断AI 解读

Inferring underlying cooperative and competitive strategies from human behaviour in repeated games is important for accurately characterizing human behaviour and understanding how people reason strategically. Finite automata, a bounded model of computation, have been extensively used to compactly represent strategies for these games and are a standard tool in game theoretic analyses. However, inference over these strategies in repeated games is challenging since the number…

近似贝叶斯计算的渐近性质AI 解读

Approximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, the limiting shape of the posterior distribution, and the asymptotic distribution of the posterior mean. These results hold under given rates…

贝叶斯空间单调多元回归AI 解读

We consider monotonic, multiple regression for contiguous regions. The regression functions vary regionally and may exhibit spatial structure. We develop Bayesian nonparametric methodology that permits estimation of both continuous and discontinuous functional shapes using marked point process and reversible jump Markov chain Monte Carlo techniques. Spatial dependence is incorporated by a flexible prior distribution which is tuned using crossvalidation and Bayesian…

线性模型中的贝叶斯因子AI 解读

We show that the Bayes factor for testing whether a subset of coefficients are zero in the normal linear regression model gives the uniformly most powerful test amongst the class of invariant tests discussed in Lehmann & Romano (2005) if the prior distributions for the regression coefficients are in a specific class of distributions. The priors in this class can have any elliptical distribution, with a specific scale matrix, for the subset of coefficients that are being…

AdaPT：利用辅助信息的交互式多重检验程序AI 解读

Summary We consider the problem of multiple-hypothesis testing with generic side information: for each hypothesis Hi we observe both a p-value pi and some predictor xi encoding contextual information about the hypothesis. For large-scale problems, adaptively focusing power on the more promising hypotheses (those more likely to yield discoveries) can lead to much more powerful multiple-testing procedures. We propose a general iterative framework for this problem, the adaptive…

超越可分解图的高斯图模型贝叶斯推断AI 解读

Summary Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that, when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general non-decomposable setting. Penalized likelihood inference in contrast has made tremendous gains in the past few years in terms of scalability and tractability. Bayesian inference, however, has not had the same level of success, though a…

通过随机效应方差估计同时解决多个问题AI 解读

The two-level normal hierarchical model (NHM) has played a critical role in statistical theory for the last several decades. In this paper, we propose random effects variance estimator that simultaneously (i) improves on the estimation of the related shrinkage factors, (ii) protects empirical best linear unbiased predictors (EBLUP) [same as empirical Bayes (EB)] of the random effects from the common overshrinkage problem, (iii) avoids complex bias correction in generating…

双向交叉分类模型的经验贝叶斯估计AI 解读

We develop an empirical Bayes procedure for estimating the cell means in an unbalanced, two-way additive model with fixed effects. We employ a hierarchical model, which reflects exchangeability of the effects within treatment and within block but not necessarily between them, as suggested before by Lindley and Smith [J. R. Stat. Soc., B 34 (1972) 1–41]. The hyperparameters of this hierarchical model, instead of considered fixed, are to be substituted with data-dependent…