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贝叶斯动态因子模型与投资组合配置AI 解读

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of…

短论：无高斯成分的 Lévy 过程表示AI 解读

We consider Lévy processes without Gaussian components, known as pure jump processes, with a view to simulating such a process in order to implement full Bayesian nonparametric analyses involving the modelling of continuous time stochastic processes. In particular, from practical and theoretical perspectives, we investigate a recent representation of Wolpert & Ickstadt (1998).

近似狄利克雷与贝塔双参数过程层级模型中的 MCMCAI 解读

We present some easy-to-construct random probability measures which approximate the Dirichlet process and an extension which we will call the beta two-parameter process. The nature of these constructions makes it simple to implement Markov chain Monte Carlo algorithms for fitting nonparametric hierarchical models. For the Dirichlet process, we consider a truncation approximation as well as a weak limit approximation based on a mixture of Dirichlet processes. The same type of…

用于贝叶斯计算的广义 Gibbs 抽样与多重网格蒙特卡洛AI 解读

Although Monte Carlo methods have frequently been applied with success, indiscriminate use of Markov chain Monte Carlo leads to unsatisfactory performances in numerous applications. We present a generalised version of the Gibbs sampler that is based on conditional moves along the traces of groups of transformations in the sample space. We explore its connection with the multigrid Monte Carlo method and its use in designing more efficient samplers. The generalised Gibbs…

动态马尔可夫转换因子模型的商业周期测度：贝叶斯模拟评估AI 解读

A Markov switching common factor is used to drive a dynamic factor model for important macroeconomic variables in eight countries. Bayesian estimation of the model is based on Markov chain Monte Carlo simulation methods which yield inferences about the unobservable path of the common factor, the latent variable of the state process and all model parameters. Additionally, simulation based filtering provides us with samples from the prediction density that can be used for…

不依赖反事实的因果推断AI 解读

A popular approach to the framing and answering of causal questions relies on the idea of counterfactuals: Outcomes that would have been observed had the world developed differently; for example, if the patient had received a different treatment. By definition, one can never observe such quantities, nor assess empirically the validity of any modeling assumptions made about them, even though one's conclusions may be sensitive to these assumptions. Here I argue that for making…

使用马尔可夫链蒙特卡洛构建系统发育树AI 解读

We describe a Bayesian method based on Markov chain simulation to study the phylogenetic relationship in a group of DNA sequences. Under simple models of mutational events, our method produces a Markov chain whose stationary distribution is the conditional distribution of the phylogeny given the observed sequences. Our algorithm strikes a reasonable balance between the desire to move globally through the space of phylogenies and the need to make computationally feasible…

融合人口普查、覆盖调查与人口学分析的贝叶斯方法AI 解读

Demographic analysis of data on births, deaths, and migration and coverage measurement surveys that use capture-recapture methods have both been used to assess U.S. Census counts. These approaches have established that unadjusted Census counts are seriously flawed for groups such as young and middle-aged African-American men. There is considerable interest in methods that combine information from the Census, coverage measurement surveys, and demographic information to…

贝叶斯方法：面向统计学家与跨学科研究者的分析AI 解读

1. Introductory statistical concepts 2. The discrete version of Bayes' theorem 3. Models with a single unknown parameter 4. The expected utility hypothesis and its alternatives 5. Models with several unknown parameters 6. Prior structures, posterior smoothing, and Bayes-Stein estimation Guide to worked examples Guide to self-study exercises.

融合人口普查、覆盖测量调查与人口学分析的贝叶斯方法AI 解读

Demographic analysis of data on births, deaths, and migration and coverage measurement surveys that use capture-recapture methods have both been used to assess U.S. Census counts. These approaches have established that unadjusted Census counts are seriously flawed for groups such as young and middle-aged African-American men. There is considerable interest in methods that combine information from the Census, coverage measurement surveys, and demographic information to…

泊松过程的序贯检验问题AI 解读

We present the explicit solution of the Bayesian problem of sequential testing of two simple hypotheses about the intensity of an observed Poisson process. The method of proof consists of reducing the initial problem to a free-boundary differential-difference Stephan problem and solving the latter by use of the principles of smooth and continuous fit. A rigorous proof of the optimality of the Wald’s sequential probability ratio test in the variational formulation of the…

凯恩斯式冲击与索洛残差：识别经济周期波动来源AI 解读

We employ a neoclassical business-cycle model to study two sources of business-cycle fluctuations: marginal efficiency of investment shocks, and total factor productivity shocks. The parameters of the model are estimated using a Bayesian procedure that accommodates prior uncertainty about their magnitudes; from these estimates, posterior distributions of the two shocks are obtained. The postwar US experience suggests that both shocks are important in understanding…

正态均值的经验贝叶斯检验AI 解读

We exhibit an empirical Bayes test $\delta_n^*$ for the normal mean testing problem using a linear error loss. Under the condition that the critical point of a Bayes test is within some known compact interval, $\delta_n^*$ is shown to be asymptotically optimal and its associated regret Bayes risk converges to zero at a rate $O(n^{-1}(\ln n)^{1.5})$, where $n$ is the number of past experiences available when the current component decision problem is considered. Under the same…

若干非参数问题的贝叶斯视角AI 解读

We study the Bayesian approach to nonparametric function estimation problems such as nonparametric regression and signal estimation. We consider the asymptotic properties of Bayes procedures for conjugate (= Gaussian) priors. We show that so long as the prior puts nonzero measure on the very large parameter set of interest then the Bayes estimators are not satisfactory. More specifically, we show that these estimators do not achieve the correct minimax rate over norm bounded…

后验分布的收敛速率AI 解读

We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinite-dimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, log-spline models, Dirichlet processes and interval censoring.

动态双变量混合模型的贝叶斯分析：能否解释收益与交易量的行为？AI 解读

Bivariate mixture models attribute the well-known positive correlation between return volatility and trading volume in financial markets to stochastic changes in a single latent variable representing the number of information arrivals. In this article, dynamic bivariate mixture models that allow for autocorrelation in the latent variable are analyzed by a Bayesian method via Markov-chain Monte Carlo techniques. The results, based on daily data from the Nikkei 225 stock-index…

GARCH 与 EGARCH 模型的完整贝叶斯推断AI 解读

A full Bayesian analysis of GARCH and EGARCH models is proposed consisting of parameter estimation, model selection, and volatility prediction. The Bayesian paradigm is implemented via Markov-chain Monte Carlo methodologies. We provide implementation details and illustrations using the General Index of the Athens stock exchange.

贝叶斯投资组合选择：标普500指数1970—1996年的实证分析AI 解读

In this article we present a technique for implementing large-scale optimal portfolio selection. We use high-frequency daily data to capture valuable statistical information in asset returns. We describe several statistical issues involved in quantitative approaches to portfolio selection. Our methodology applies to large-scale portfolio-selection problems in which the number of possible holdings is large relative to the estimation period provided by historical data. We…

通过混合建模开展因子实验的贝叶斯分析AI 解读

A Bayesian analysis for factorial experiments is presented, using finite mixture distributions to model the main effects and interactions. This allows both estimation and an analogue of hypothesis testing in a posterior analysis using a single prior specification. A detailed formulation based on this approach is provided for the case of the two-way model with replication, allowing interactions. Issues in formulating a suitable prior are discussed in detail, and, in the…

一类单纯形约束下的离散分布估计AI 解读

Simplex constraints, such as monotonicity and convexity or concavity on the probabilities of a set of discrete distributions, are useful for modeling and analyzing discrete data. This article considers both maximum likelihood estimation and Bayesian estimation of discrete distribution with a class of simplex constraints using the Expectation-Maximization (EM) algorithm and the data augmentation (DA) algorithm. The formulation and implementation of EM and DA for binomial,…

广义线性混合模型的参考贝叶斯方法AI 解读

Bayesian methods furnish an attractive approach to inference in generalized linear mixed models. In the absence of subjective prior information for the random-effect variance components, these analyses are typically conducted using either the standard invariant prior for normal responses or diffuse conjugate priors. Previous work has pointed out serious difficulties with both strategies, and we show here that as in normal mixed models, the standard invariant prior leads to…

Metropolis 抽样中的多重尝试方法与局部优化AI 解读

This article describes a new Metropolis-like transition rule, the multiple-try Metropolis, for Markov chain Monte Carlo (MCMC) simulations. By using this transition rule together with adaptive direction sampling, we propose a novel method for incorporating local optimization steps into a MCMC sampler in continuous state-space. Numerical studies show that the new method performs significantly better than the traditional Metropolis-Hastings (M-H) sampler. With minor tailoring…

马尔可夫链蒙特卡洛：用于贝叶斯推断的随机模拟AI 解读

Introduction Stochastic simulation Introduction Generation of Discrete Random Quantities Generation of Continuous Random Quantities Generation of Random Vectors and Matrices Resampling Methods Exercises Bayesian Inference Introduction Bayes' Theorem Conjugate Distributions Hierarchical Models Dynamic Models Spatial Models Model Comparison Exercises Approximate methods of inference Introduction Asymptotic Approximations Approximations by Gaussian Quadrature Monte Carlo…

具有一类单纯形约束的离散分布估计AI 解读

Simplex constraints, such as monotonicity and convexity or concavity on the probabilities of a set of discrete distributions, are useful for modeling and analyzing discrete data. This article considers both maximum likelihood estimation and Bayesian estimation of discrete distribution with a class of simplex constraints using the Expectation-Maximization (EM) algorithm and the data augmentation (DA) algorithm. The formulation and implementation of EM and DA for binomial,…