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给定充分统计量的蒙特卡洛条件化AI 解读

In this paper we derive general formulae suitable for Monte Carlo computation of conditional expectations of functions of a random vector given a sufficient statistic. The problem of direct sampling from the conditional distribution is considered in particular. It is shown that this can be done by a simple parameter adjustment of the original statistical model, provided the model has a certain pivotal structure. A connection with a classical problem regarding fiducial and…

袋装最近邻分类器的性质AI 解读

Summary It is shown that bagging, a computationally intensive method, asymptotically improves the performance of nearest neighbour classifiers provided that the resample size is less than 69% of the actual sample size, in the case of with-replacement bagging, or less than 50% of the sample size, for without-replacement bagging. However, for larger sampling fractions there is no asymptotic difference between the risk of the regular nearest neighbour classifier and its bagged…

含时变成分的结构学习：追踪金融时间序列的横截面结构AI 解读

Summary When modelling multivariate financial data, the problem of structural learning is compounded by the fact that the covariance structure changes with time. Previous work has focused on modelling those changes by using multivariate stochastic volatility models. We present an alternative to these models that focuses instead on the latent graphical structure that is related to the precision matrix. We develop a graphical model for sequences of Gaussian random vectors when…

用于模式识别的层级检验设计AI 解读

We explore the theoretical foundations of a “twenty questions” approach to pattern recognition. The object of the analysis is the computational process itself rather than probability distributions (Bayesian inference) or decision boundaries (statistical learning). Our formulation is motivated by applications to scene interpretation in which there are a great many possible explanations for the data, one (“background”) is statistically dominant, and it is imperative to…

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基于总体校准的基因表型刻画AI 解读

Phenotypic characterization of rare disease genes poses a significant statistical challenge, but the need to do so is clear. Clinical management of patients carrying a disease gene depends crucially on an accurate characterization of the genetically predisposed disease, including its likelihood of occurrence among mutation carriers, natural history, and response to treatment. We propose a formal yet practical method for controlling for bias due to ignoring ascertainment,…

利用辅助变量构造平稳时间序列模型及其应用AI 解读

Here we present a novel method for modeling stationary time series. Our approach is to construct the model with a specified marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation structure. We construct models that parallel existing structures, namely state-space models, autoregressive conditional heteroscedasticity (ARCH) models, and generalized ARCH models. We use Bayesian techniques to…

计数数据的贝叶斯半参数保序回归AI 解读

This article proposes a semiparametric Bayesian approach for inference on an unknown isotonic regression function, f(x), characterizing the relationship between a continuous predictor, X, and a count response variable, Y, adjusting for covariates, Z. A Dirichlet process mixture of Poisson distributions is used to avoid parametric assumptions on the conditional distribution of Y given X and Z. Then, to also avoid parametric assumptions on f(x), a novel prior formulation is…

暴露缺失时匹配病例对照研究的半参数贝叶斯分析AI 解读

This article considers Bayesian analysis of matched case-control problems when one of the covariates is partially missing. Within the likelihood context, the standard approach to this problem is to posit a fully parametric model among the controls for the partially missing covariate as a function of the covariates in the model and the variables making up the strata. Sometimes the strata effects are ignored at this stage. Our approach differs not only in that it is Bayesian,…

概率分布引出的统计方法AI 解读

Elicitation is a key task for subjectivist Bayesians. Although skeptics hold that elicitation cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subject-matter expert colleagues. This article reviews the state of the art, reflecting the experience of statisticians informed by the fruits of a long line of psychological research into how people represent uncertain information cognitively and how they respond to questions…

利用分片高斯过程分析非平稳空间数据AI 解读

In many problems in geostatistics the response variable of interest is strongly related to the underlying geology of the spatial location. In these situations there is often little correlation in the responses found in different rock strata, so the underlying covariance structure shows sharp changes at the boundaries of the rock types. Conventional stationary and nonstationary spatial methods are inappropriate, because they typically assume that the covariance between points…

结合ACE调查与人口分析评估2000年人口普查的贝叶斯方法AI 解读

Demographic analysis of data on births, deaths, and migration, together with coverage measurement surveys that use capture-recapture methods, have established that U.S. Census counts are flawed for certain subpopulations. Previous work using 1990 Census data in African-Americans age 30–49 proposed a hierarchical Bayesian model that assembled Census, follow-up survey, and demographic data, providing a principled solution to the problem of negative estimated counts in some…

高维数据聚类中的贝叶斯变量选择AI 解读

Over the last decade, technological advances have generated an explosion of data with substantially smaller sample size relative to the number of covariates (p ≫ n). A common goal in the analysis of such data involves uncovering the group structure of the observations and identifying the discriminating variables. In this article we propose a methodology for addressing these problems simultaneously. Given a set of variables, we formulate the clustering problem in terms of a…

利用基因表达数据进行肿瘤的贝叶斯分类AI 解读

Summary Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards…

Antithetic coupling is a general stratification strategy for reducing Monte Carlo variance without increasing the simulation size. The use of the antithetic principle in the Monte Carlo literature typically employs two strata via antithetic quantile coupling. We demonstrate here that further stratification, obtained by using k>2 (e.g., k=3–10) antithetically coupled variates, can offer substantial additional gain in Monte Carlo efficiency, in terms of both variance and bias.…

正态层级模型中后验的适当性与超先验的可容许性AI 解读

Hierarchical modeling is wonderful and here to stay, but hyperparameter priors are often chosen in a casual fashion. Unfortunately, as the number of hyperparameters grows, the effects of casual choices can multiply, leading to considerably inferior performance. As an extreme, but not uncommon, example use of the wrong hyperparameter priors can even lead to impropriety of the posterior. For exchangeable hierarchical multivariate normal models, we first determine when a…

基于尖峰—平板先验的变量选择：频率学派与贝叶斯策略AI 解读

Variable selection in the linear regression model takes many apparent faces from both frequentist and Bayesian standpoints. In this paper we introduce a variable selection method referred to as a rescaled spike and slab model. We study the importance of prior hierarchical specifications and draw connections to frequentist generalized ridge regression estimation. Specifically, we study the usefulness of continuous bimodal priors to model hypervariance parameters, and the…

流形上的不变贝叶斯估计AI 解读

A frequent and well-founded criticism of the maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter γ taking values in a differentiable manifold Γ is that they are not invariant to arbitrary “reparameterizations” of Γ. This paper clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a sine qua non for a mathematically well-defined problem, and diffeomorphism…

高斯过程的默认先验AI 解读

Motivated by the statistical evaluation of complex computer models, we deal with the issue of objective prior specification for the parameters of Gaussian processes. In particular, we derive the Jeffreys-rule, independence Jeffreys and reference priors for this situation, and prove that the resulting posterior distributions are proper under a quite general set of conditions. A proper flat prior strategy, based on maximum likelihood estimates, is also considered, and all…

股权溢价的结构突变AI 解读

This article uses Bayesian marginal likelihood analysis to compare univariate models of the stock return behavior and test for structural breaks in the equity premium. The analysis favors a model that relates the equity premium to Markov-switching changes in the level of market volatility and accommodates volatility feedback. For this model, there is evidence of a one-time structural break in the equity premium in the 1940s, with no evidence of additional breaks in the…

协整的贝叶斯参考分析AI 解读

A Bayesian reference analysis of the cointegrated vector autoregression is presented based on a new prior distribution. Among other properties, it is shown that this prior distribution distributes its probability mass uniformly over all cointegration spaces for a given cointegration rank and is invariant to the choice of normalizing variables for the cointegration vectors. Several methods for computing the posterior distribution of the number of cointegrating relations and…

基本区域层级模型下小区域估计量的变异性度量AI 解读

In this paper based on a basic area level model we obtain second-order accurate approximations to the mean squared error of model-based small area estimators, using the Fay & Herriot (1979) iterative method of estimating the model variance based on weighted residual sum of squares. We also obtain mean squared error estimators unbiased to second order. Based on simulations, we compare the finite-sample performance of our mean squared error estimators with those based on…

平滑秩估计量的标准误与协方差矩阵AI 解读

A ‘pseudo-Bayesian’ interpretation of standard errors yields a natural induced smoothing of statistical estimating functions. When applied to rank estimation, the lack of smoothness which prevents standard error estimation is remedied. Efficiency and robustness are preserved, while the smoothed estimation has excellent computational properties. In particular, convergence of the iterative equation for standard error is fast, and standard error calculation becomes…