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二项回归模型中 Jeffreys 先验的性质与计算实现AI 解读

We study several theoretical properties of Jeffreys's prior for binomial regression models. We show that Jeffreys's prior is symmetric and unimodal for a class of binomial regression models. We characterize the tail behavior of Jeffreys's prior by comparing it with the multivariate t and normal distributions under the commonly used logistic, probit, and complementary log-log regression models. We also show that the prior and posterior normalizing constants under Jeffreys's…

结构性无应答下多层调查数据的域级协方差分析AI 解读

Health care quality surveys in the United States are administered to individual respondents (i.e., hospital patients, health plan members) to evaluate the performance of health care organizations (i.e., hospitals, health plans), which thus constitute estimation domains. For better understanding and more parsimonious reporting of dimensions of quality, we analyze relationships among quality measures at the domain level. Rather than specifying a full parametric model for the…

随机矩阵理论：SAMSI 研究计划介绍AI 解读

Craig Tracy and I became coorganizers and the program's scope was enlarged.It now included regularization issues, inference in graphical models, Bayesian multivariate analysis in such models, applications of random matrix theory particularly in electrical engineering, as well as applications of regularization and high dimensional multivariate analysis in geophysical models.The program was realized as a six-month session in 2006-2007 and featured not only the usual opening…

高斯图模型中的灵活协方差估计AI 解读

In this paper, we propose a class of Bayes estimators for the covariance matrix of graphical Gaussian models Markov with respect to a decomposable graph G. Working with the WPG family defined by Letac and Massam [Ann. Statist. 35 (2007) 1278–1323] we derive closed-form expressions for Bayes estimators under the entropy and squared-error losses. The WPG family includes the classical inverse of the hyper inverse Wishart but has many more shape parameters, thus allowing for…

用于多项选择的贝叶斯混合 Logit–Probit 模型AI 解读

In this paper, we introduce a new flexible mixed model for multinomial discrete choice where the key individual- and alternative-specific parameters of interest are allowed to follow an assumption-free nonparametric density specification, while other alternative-specific coefficients are assumed to be drawn from a multivariate Normal distribution, which eliminates the independence of irrelevant alternatives assumption at the individual level. A hierarchical specification of…

消费者偏好与需求系统AI 解读

This paper is an up-to-date survey of the state-of-the-art in consumer demand modelling. We review and evaluate advances in a number of related areas, including different approaches to empirical demand analysis, such as the differential approach, the locally flexible functional forms approach, the semi-non-parametric approach, and a non-parametric approach. We also address estimation issues, including sampling theoretic and Bayesian estimation methods, and discuss the…

随机序约束下的贝叶斯非参数推断AI 解读

This article considers Bayesian inference about collections of unknown distributions subject to a partial stochastic ordering. To address problems in testing of equalities between groups and estimation of group-specific distributions, we propose classes of restricted dependent Dirichlet process priors. These priors have full support in the space of stochastically ordered distributions, and can be used for collections of unknown mixture distributions to obtain a flexible…

贝叶斯假设检验中基于 Jeffreys 散度的先验推广AI 解读

Summary We introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence-based (DB) priors. DB priors have simple forms and desirable properties like information (finite sample) consistency and are often similar to other existing proposals like intrinsic priors. Moreover, in normal linear model scenarios, they reproduce the Jeffreys–Zellner–Siow priors exactly.…

澄清：回归模型选择的残差似然方法AI 解读

In regression models, Shi and Tsai (2002) applied the logarithm of the commonly used residual maximum likelihood function (2.2) to obtain the residual information criterion RIC in equation (2.10). Function (2.2) is the same as the modified profile likelihood function, but it is different from the complete residual maximum likelihood function that contains two additional terms, −12(n−k)log⁡(2π) and 12log⁡|X′X|⁠, on the right-hand side of expression (2.2). Because these two…

强依赖过程的非线性模型及金融应用AI 解读

This paper is motivated by recent evidence that many univariate economic and financial time series have both nonlinear and long memory characteristics. Hence, this paper considers a general nonlinear, smooth transition regime autoregression which is embedded within a strongly dependent, long memory process. A time domain MLE with simultaneous estimation of the long memory, linear AR and nonlinear parameters is shown to have desirable asymptotic properties. The Bayesian and…

通过马尔可夫链论证评价形式后验分布AI 解读

We consider evaluation of proper posterior distributions obtained from improper prior distributions. Our context is estimating a bounded function φ of a parameter when the loss is quadratic. If the posterior mean of φ is admissible for all bounded φ, the posterior is strongly admissible. We give sufficient conditions for strong admissibility. These conditions involve the recurrence of a Markov chain associated with the estimation problem. We develop general sufficient…

利用 Malliavin 演算的高斯过程漂移 Stein 估计AI 解读

We consider the nonparametric functional estimation of the drift of a Gaussian process via minimax and Bayes estimators. In this context, we construct superefficient estimators of Stein type for such drifts using the Malliavin integration by parts formula and superharmonic functionals on Gaussian space. Our results are illustrated by numerical simulations and extend the construction of James–Stein type estimators for Gaussian processes by Berger and Wolpert [J. Multivariate…

高维分类与数据挖掘中的 Gibbs 后验变量选择AI 解读

In the popular approach of “Bayesian variable selection” (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is constructed from a risk function of practical interest (such as the classification error) and aims at minimizing a risk function without modeling the data…

大型多方程马尔可夫切换模型的推断方法AI 解读

Inference for multiple-equation Markov-chain models raises a number of difficulties that are unlikely to appear in smaller models. Our framework allows for many regimes in the transition matrix, without letting the number of free parameters grow as the square as the number of regimes, but also without losing a convenient form for the posterior distribution. Calculation of marginal data densities is difficult in these high-dimensional models. This paper gives methods to…

大量预测变量下的预测：贝叶斯收缩能否替代主成分？AI 解读

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study conditions for consistency of the forecast based on Bayesian regression as the cross-section and the sample size become large. This analysis…

贝叶斯模型平均与汇率预测AI 解读

Exchange rate forecasting is hard and the seminal result of Meese and Rogoff [Meese, R., Rogoff, K., 1983. Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics 14, 3-24] that the exchange rate is well approximated by a driftless random walk, at least for prediction purposes, still stands despite much effort at constructing other forecasting models. However, in several other macro and financial forecasting…

动态 OLS 协整回归中超前与滞后阶数的数据驱动选择AI 解读

Saikkonen (1991, Econometric Theory 7, 1–21) developed an asymptotic optimality theory for the estimation of cointegrated regressions. He proposed the dynamic ordinary least squares (OLS) estimator obtained by augmenting the static cointegrating regression with leads and lags of the first differences of the I(1) regressors. However, the assumptions imposed preclude the use of information criteria such as the Akaike information criterion (AIC) and Bayesian information…

线性模型中无限多个对比的 Benjamini–Hochberg 方法AI 解读

Benjamini and Hochberg's method for controlling the false discovery rate is applied to the problem of testing infinitely many contrasts in linear models. Exact, easily calculated critical values are derived, defining a new multiple comparisons method for testing contrasts in linear models. The method is adaptive, depending on the data through the F-statistic, like the Waller–Duncan Bayesian multiple comparisons method. Comparisons with Scheffé's method are given, and the…

影响函数与稳健贝叶斯、经验贝叶斯小区域估计AI 解读

We introduce new robust small area estimation procedures based on area-level models. We first find influence functions corresponding to each individual area-level observation by measuring the divergence between the posterior density functions of regression coefficients with and without that observation. Next, based on these influence functions, properly standardized, we propose some new robust Bayes and empirical Bayes small area estimators. The mean squared errors and…

大模型空间下用于模型选择的扩展贝叶斯信息准则AI 解读

The ordinary Bayesian information criterion is too liberal for model selection when the model space is large. In this paper, we re-examine the Bayesian paradigm for model selection and propose an extended family of Bayesian information criteria, which take into account both the number of unknown parameters and the complexity of the model space. Their consistency is established, in particular allowing the number of covariates to increase to infinity with the sample size.…

效应估计中的调整不确定性AI 解读

Often there is substantial uncertainty in the selection of confounders when estimating the association between an exposure and health. We define this type of uncertainty as `adjustment uncertainty'. We propose a general statistical framework for handling adjustment uncertainty in exposure effect estimation for a large number of confounders, we describe a specific implementation, and we develop associated visualization tools. Theoretical results and simulation studies show…

随机波动率模型期权价格计算的高斯近似方案AI 解读

We consider European options on a price process that follows the log-linear stochastic volatility model. Two stochastic integrals in the option pricing formula are costly to compute. We derive a central limit theorem to approximate them. At parameter settings appropriate to foreign exchange data our formulas improve computation speed by a factor of 1000 over brute force Monte Carlo making MCMC statistical methods practicable. We provide estimates of model parameters from…

利用汇总数据解释个体响应AI 解读

Empirical analysis of individual response behavior is sometimes limited due to the lack of explanatory variables at the individual level. In this paper we put forward a new approach to estimate the effects of covariates on individual response, where the covariates are unknown at the individual level but observed at some aggregated level. This situation may, for example, occur when the response variable is available at the household level but covariates only at the zip-code…

同时估计异质性的贝叶斯捕获再捕获种群模型AI 解读

We develop a Bayesian capture-recapture model that provides estimates of abundance as well as time-varying and heterogeneous survival and capture probability distributions. The model uses a state-space approach by incorporating an underlying population model and an observation model, and here is applied to photo-identification data to estimate trends in the abundance and survival of a population of bottlenose dolphins (Tursiops truncatus) in northeast Scotland. Novel…