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多准则决策合并：原住民捕鲸管理程序的竞争性开发AI 解读

International Whaling Commission management of aboriginal subsistence whaling will eventually use an aboriginal whaling management procedure (AWMP) chosen from a collection of candidate procedures after grueling simulation testing. An AWMP is a fully automatic algorithm designed to operate on the results of an assessment (i.e., a statistical estimation problem relying on sparse series of whale abundance data) to produce a catch limit in each year of real or simulated…

摘要暂缺

When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's finite-sample properties, derived here, can depart substantially from the standard regression setting. Bayesian posterior distributions for the regression parameters are obtained under specifications that differ with respect to (i) prior beliefs about the autocorrelation of the regressor…

小波系数的协方差结构：理论与贝叶斯建模AI 解读

Summary We present theoretical results on the random wavelet coefficients covariance structure. We use simple properties of the coefficients to derive a recursive way to compute the within- and across-scale covariances. We point out a useful link between the algorithm proposed and the two-dimensional discrete wavelet transform. We then focus on Bayesian wavelet shrinkage for estimating a function from noisy data. A prior distribution is imposed on the coefficients of the…

通常似然与调整似然所对应的置信区间AI 解读

Summary In the presence of nuisance parameters, we drive an explicit higher order asymptotic formula to compare the expected lengths of confidence intervals given by likelihood ratio statistics arising from the usual profile likelihood and various adjustments thereof. Highest posterior density regions, with approximate frequentist validity, are also included in the study.

自回归时间序列模型的先验与成分结构AI 解读

Summary New approaches to prior specification and structuring in autoregressive time series models are introduced and developed. We focus on defining classes of prior distributions for parameters and latent variables related to latent components of an autoregressive model for an observed time series. These new priors naturally permit the incorporation of both qualitative and quantitative prior information about the number and relative importance of physically meaningful…

利用 Fourier 表示进行半参数回归的贝叶斯推断AI 解读

Summary This paper presents the Bayesian analysis of a semiparametric regression model that consists of parametric and nonparametric components. The nonparametric component is represented with a Fourier series where the Fourier coefficients are assumed a priori to have zero means and to decay to 0 in probability at either algebraic or geometric rates. The rate of decay controls the smoothness of the response function. The posterior analysis automatically selects the amount…

贝叶斯—置信域的构造AI 解读

Summary We obtain approximate Bayes–confidence intervals for a scalar parameter based on directed likelihood. The posterior probabilities of these intervals agree with their unconditional coverage probabilities to fourth order, and with their conditional coverage probabilities to third order. These intervals are constructed for arbitrary smooth prior distributions. A key feature of the construction is that log-likelihood derivatives beyond second order are not required,…

仅基于似然函数的概率上界AI 解读

Summary In the problem of parametric statistical inference with a finite parameter space, we propose some simple rules for defining posterior upper and lower probabilities directly from the observed likelihood function, without using any prior information. The rules satisfy the likelihood principle and a basic consistency principle (‘avoiding sure loss’), they produce vacuous inferences when the likelihood function is constant, and they have other symmetry, monotonicity and…

农业田间试验的贝叶斯分析AI 解读

Summary The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analysed with comparative ease, by using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations…

贝叶斯分析、计算与交流软件 BACCAI 解读

Bayesian Analysis, Computation and Communication (BACC) is a new Bayesian software package which is linked to Gauss and takes the form of a set of Gauss commands. In this review, I outline a list of qualities that a Bayesian software package should have. I then discuss whether BACC has these qualities in the context of a brief description and an extended example.

对称最低价密封投标拍卖：共同价值与独立私人价值范式的后验赔率比较AI 解读

I attempt to decide, using the posterior odds ratio, whether the symmetric common-value paradigm or the symmetric independent-private-values paradigm is a more probable explanation of the low-price, sealed-bid auctions conducted by the Indian Oil Corporation to purchase crude-oil from the international market. The estimation approach is structural parametric. The auctions are modelled as static non-cooperative games of incomplete information with risk neutral bidders. I…

随机波动率模型中的贝叶斯单位根检验AI 解读

This article uses a Bayesian unit-root test in stochastic volatility models. The time series of interest is the volatility that is unobservable. The unit-root testing is based on the posterior odds ratio, which is approximated by Markov-chain Monte Carlo methods. Simulations show that the testing procedure is efficient for moderate sample size. The unit-root hypothesis is rejected in seven market indexes, and some evidence of nonstationarity is observed in the TWSI of Taiwan.

购买力平价中对称性与比例性的面板检验AI 解读

Purchasing power parity (PPP) is a proposition equating the nominal exchange rate to the ratio of the domestic to foreign price levels. This article employs Bayesian panel-data methods to test for PPP, in particular, the implied symmetry and proportionality conditions. Using a dataset of all the Organization for Economic Cooperation and Development member countries in the post-Bretton Woods era, I do not find support for symmetry and proportionality in PPP over time and…

含马尔可夫转换与时变增长的GDP不可观测成分模型：贝叶斯分析AI 解读

We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two time-varying drift components. One drift represents the expected rate of growth during recession; the other drift represents the expected rate during expansion. Estimates indicate a substantial decline in the latter annual rate for the United States from 6.4% in 1950 to 3.6% by 1990. We…

层级与图式对数线性模型的 MCMC 模型选择AI 解读

We use reversible jump Markov chain Monte Carlo methods (Green, 1995) to develop strategies for calculating posterior probabilities of hierarchical, graphical or decomposable log-linear models for high-dimensional contingency tables. Even for tables of moderate size, these sets of models may be very large. The choice of suitable prior distributions for model parameters is also discussed in detail, and two examples are presented. For the first example, a three-way table, the…

多元回归变量选择的非共轭贝叶斯决策论方法AI 解读

We consider the choice of explanatory variables in multivariate linear regression. Our approach balances prediction accuracy against costs attached to variables in a multivariate version of a decision theory approach pioneered by Lindley (1968). We also employ a non-conjugate proper prior distribution for the parameters of the regression model, extending the standard normal-inverse Wishart by adding a component of error which is unexplainable by any number of predictor…

随机分布及相关函数的贝叶斯非参数推断AI 解读

Summary In recent years, Bayesian nonparametric inference, both theoretical and computational, has witnessed considerable advances. However, these advances have not received a full critical and comparative analysis of their scope, impact and limitations in statistical modelling; many aspects of the theory and methods remain a mystery to practitioners and many open questions remain. In this paper, we discuss and illustrate the rich modelling and analytic possibilities that…

泊松过程的贝叶斯多尺度模型AI 解读

I introduce a class of Bayesian multiscale models (BMSM's) for one-dimensional inhomogeneous Poisson processes. The focus is on estimating the (discretized) intensity function underlying the process. Unlike the usual transform-based approach at the heart of most wavelet-based methods for Gaussian data, these BMSM's are constructed using recursive dyadic partitions (RDP's) within an entirely likelihood-based framework. Each RDP may be associated with a binary tree, and a new…

含治愈比例生存数据的新贝叶斯模型AI 解读

We consider Bayesian methods for right-censored survival data for populations with a surviving (cure) fraction. We propose a model that is quite different from the standard mixture model for cure rates. We provide a natural motivation and interpretation of the model and derive several novel properties of it. First, we show that the model has a proportional hazards structure, with the covariates depending naturally on the cure rate. Second, we derive several properties of the…

利用数据驱动先验进行加性非参数回归的变量选择与函数估计AI 解读

A hierarchical Bayesian approach is proposed for variable selection and function estimation in additive nonparametric Gaussian regression models and additive nonparametric binary regression models. The prior for each component function is an integrated Wiener process resulting in a posterior mean estimate that is a cubic smoothing spline. Each of the explanatory variables is allowed to be in or out of the model, and the regression functions are estimated by model averaging.…

用信号提取方法建模具有脉冲与变化基线的激素时间序列AI 解读

Hormones serve as regulating signals for many biological processes. In recent years, it was determined that many hormones are secreted in a pulsatile manner and that the pulsatile secretion pattern, in addition to the absolute concentration level, is important in regulating biological processes. Consequently, it is necessary to characterize the latent secretion patterns from measurements of concentration levels. The characterization is complicated by the presence of a…

泊松过程的贝叶斯多尺度模型AI 解读

I introduce a class of Bayesian multiscale models (BMSM's) for one-dimensional inhomogeneous Poisson processes. The focus is on estimating the (discretized) intensity function underlying the process. Unlike the usual transform-based approach at the heart of most wavelet-based methods for Gaussian data, these BMSM's are constructed using recursive dyadic partitions (RDP's) within an entirely likelihood-based framework. Each RDP may be associated with a binary tree, and a new…

含脉冲与变化基线的激素时间序列信号提取方法AI 解读

Hormones serve as regulating signals for many biological processes. In recent years, it was determined that many hormones are secreted in a pulsatile manner and that the pulsatile secretion pattern, in addition to the absolute concentration level, is important in regulating biological processes. Consequently, it is necessary to characterize the latent secretion patterns from measurements of concentration levels. The characterization is complicated by the presence of a…