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动态二元混合模型的估计AI 解读

This note compares a Bayesian Markov chain Monte Carlo approach implemented by Watanabe with a maximum likelihood ML approach based on an efficient importance sampling procedure to estimate dynamic bivariate mixture models. In these models, stock price volatility and trading volume are jointly directed by the unobservable number of price-relevant information arrivals, which is specified as a serially correlated random variable. It is shown that the efficient importance…

概率与统计史研究第四十八篇：Ernest Lhoste 的贝叶斯贡献AI 解读

The contributions of Ernest Lhoste are largely unknown outside France, and even within that country are not well known. His important contributions were in the two areas of the development of prior distributions that represent little or no information, and a sophisticated posterior analysis for normal and binomial populations. His results are similar to those of Haldane (1948) and Jeffreys (1961), but they appeared much earlier, in 1923. He gave a great deal of thought to…

Cox 部分似然的贝叶斯论证AI 解读

In this paper, we establish both naive and formal Bayesian justifications of Cox's (1975) partial likelihood and its various modifications. We extend the original work of Kalbfieisch (1978), who showed that the partial likelihood is a limiting marginal posterior under noninformative priors for baseline hazards. We extend the result to scenarios with time‐dependent covariates and time‐varying regression parameters. We establish results for continuous time as well as grouped…

含扩散与离散分量的马尔可夫过程的贝叶斯推断AI 解读

Data arising in certain radio‐tracking experiments consist of both a continuous spatial component and a discrete component related to behaviour. This leads naturally to stochastic models with a state space which is a product of continuous and discrete components. We consider a class of such models in continuous time, which can be thought of as diffusions in random environments. They are related to switching diffusion or hidden Markov models, but observations are made on both…

因果推断中的一致一致性AI 解读

There is a long tradition of representing causal relationships by directed acyclic graphs (Wright, 1934). Spirtes (1994), Spirtes et al. (1993) and Pearl & Verma (1991) describe procedures for inferring the presence or absence of causal arrows in the graph even if there might be unobserved confounding variables, and/or an unknown time order, and that under weak conditions, for certain combinations of directed acyclic graphs and probability distributions, are asymptotically,…

结肠癌发生中层级函数数据的小波非参数建模AI 解读

In this article we develop new methods for analyzing the data from an experiment using rodent models to investigate the effect of type of dietary fat on O6-methylguanine-DNA-methyltransferase (MGMT), an important biomarker in early colon carcinogenesis. The data consist of observed profiles over a spatial variable contained within a two-stage hierarchy, a structure that we dub hierarchical functional data. We present a new method providing a unified framework for modeling…

购买数量与时机的联合分布模型AI 解读

Prediction of purchase timing and quantity decisions of a household is an important element for success of any retailer. This is especially so for an online retailer, as the traditional brick-and-mortar retailer would be more concerned with total sales. A number of statistical models have been developed in the marketing literature to aid traditional retailers in predicting sales and analyzing the impact of various marketing activities on sales. However, there are two…

多维纵向数据的动态潜在特质模型AI 解读

This article presents a new approach for analysis of multidimensional longitudinal data, motivated by studies using an item response battery to measure traits of an individual repeatedly over time. A general modeling framework is proposed that allows mixtures of count, categorical, and continuous response variables. Each response is related to age-specific latent traits through a generalized linear model that accommodates item-specific measurement errors. A transition model…

遥远未来有多重要？公众减缓气候生态影响意愿的层级贝叶斯分析AI 解读

How much does the far future matter? This question lies at the heart of many important environmental policy issues, such as global climate change, biodiversity loss, and the disposal of radioactive waste. Although philosophers, experts, and others offer their viewpoints on this deep question, the solution to many environmental problems lies in the willingness of the public to bear significant costs now to make the far future a better place. Short of national plebiscites, the…

支持集依赖参数的参数化结构模型中的渐近效率AI 解读

In certain auction, search, and related models, the boundary of the support of the observed data depends on some of the parameters of interest. For such nonregular models, standard asymptotic distribution theory does not apply. Previous work has focused on characterizing the nonstandard limiting distributions of particular estimators in these models. In contrast, we study the problem of constructing efficient point estimators. We show that the maximum likelihood estimator is…

通过空间变形对非平稳空间协方差结构进行贝叶斯推断AI 解读

Summary In geostatistics it is common practice to assume that the underlying spatial process is stationary and isotropic, i.e. the spatial distribution is unchanged when the origin of the index set is translated and under rotation about the origin. However, in environmental problems, such assumptions are not realistic since local influences in the correlation structure of the spatial process may be found in the data. The paper proposes a Bayesian model to address the anisot-…

地电电阻率数据的贝叶斯反演AI 解读

Summary Enormous quantities of geoelectrical data are produced daily and often used for large scale reservoir modelling. To interpret these data requires reliable and efficient inversion methods which adequately incorporate prior information and use realistically complex modelling structures. We use models based on random coloured polygonal graphs as a powerful and flexible modelling framework for the layered composition of the Earth and we contrast our approach with earlier…

经典估计的 MCMC 方法AI 解读

This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric…

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选择模型中医院质量的贝叶斯推断AI 解读

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and nonrandom selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This…

完全贝叶斯样条平滑与内蕴自回归先验AI 解读

There is a well‐known Bayesian interpretation for function estimation by spline smoothing using a limit of proper normal priors. The limiting prior and the conditional and intrinsic autoregressive priors popular for spatial modelling have a common form, which we call partially informative normal. We derive necessary and sufficient conditions for the propriety of the posterior for this class of partially informative normal priors with noninformative priors on the variance…

Arnason–Schwarz 模型的闭式似然AI 解读

We provide a general framework for the computationally efficient analysis, both Bayesian and classical, of integrated multi‐site recovery/recapture models in the presence of individual‐level covariates by extending the basic Arnason–Schwarz models and deriving closed‐form likelihood expressions, together with corresponding sufficient statistics.

贝叶斯经验似然AI 解读

Research has shown that empirical likelihood tests have many of the same asymptotic properties as those derived from parametric likelihoods. This leads naturally to the possibility of using empirical likelihood as the basis for Bayesian inference. Different ways in which this goal might be accomplished are considered. The validity of the resultant posterior inferences is examined, as are frequentist properties of the Bayesian empirical likelihood intervals.

部分随机序约束下的贝叶斯方法AI 解读

We discuss two methods of making nonparametric Bayesian inference on probability measures subject to a partial stochastic ordering. The first method involves a nonparametric prior for a measure on partially ordered latent observations, and the second involves rejection sampling. Computational approaches are discussed for each method, and interpretations of prior and posterior information are discussed. An application is presented in which inference is made on the number of…

探索经济时间序列：贝叶斯图形方法AI 解读

Many macroeconomic time series exhibit non-stationary behaviour. When modelling such series an important problem is to assess the nature of this non-stationary behaviour. Initial interest centred on two types of linear non-stationary models, namely those for which the removal of a trend induces stationarity and those for which taking the first difference produces a stationary series. The latter are referred to as unit root models. More recently, other models such as state…

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核苷酸序列沿线系统发育变异的推断AI 解读

We develop a Bayesian multiple changepoint model to infer spatial phylogenetic variation (SPV) along aligned molecular sequence data. SPV occurs in sequences from organisms that have undergone biological recombination or when evolutionary rates and selective pressures vary along the sequences. This Bayesian approach permits estimation of uncertainty regarding recombination, the crossing-over locations, and all other model parameters. The model assumes that the sites along…

基于多元自由结点回归样条的广义非线性建模AI 解读

A Bayesian method is presented for the nonparametric modeling of univariate and multivariate non-Gaussian response data. Data-adaptive multivariate regression splines are used where the number and location of the knot points are treated as random. The posterior model space is explored using a reversible-jump Markov chain Monte Carlo sampler. Computational difficulties are partly alleviated by introducing a random residual effect in the model that leaves many of the posterior…