贝叶斯方法和频率方法比较研究

频率学派和贝叶斯学派作为统计推理中的两个最重要的方法,对于这两种对立的研究方式的站队选择显然影响着研究者的研究活动。占有霸主地位的频率学派尽管是后来者,显然占有着优势的地位,而今年以来贝叶斯方法的复苏,重新向频率派发起挑战。文章从概率的产生作为开始,简单地介绍了统计的发展历程,并分别对这两种方法选择的实用、重要的可操作标准给予分析,认为两种方法各有利弊,对两种方法的选择应结合实际情况选择。     

Education,cognitive ability,and cause-specific mortality: A structural approach

Education is negatively associated with most major causes of death. Prior work ignores the premise that cause-specific hazards are interdependent and that both education and mortality depend on cognitive ability. We analyse Swedish men aged 18–63, focusing on months lost due to specific causes—which solves the interdependence problem—and use a structural model that accounts for confounding due to cognitive ability. In a standard Cox model controlling for Intelligence Quotient, improving education is associated with large decreases in mortality for major causes of death. In the structural model, improving education is associated with a small decrease in months lost for most causes and education levels. Among the least educated, however, improving education strongly reduces the months lost, mainly those lost from external causes, such as accidents and suicide. Results suggest that conventional analysis of education and mortality may be biased, even if accounting for observed cognition.     

Causal factor relations of supply chain competitiveness via fuzzy DEMATEL method for Thai automotive industry

Abstract

The automotive industry is complex and global. An automotive supply chain involves multiple manufacturers and service providers in different parts of the chain across different countries. Diffusion of the global production system creates both opportunities and challenges for the supply chain itself as well as production base countries. Thailand is a production base for over 15 automotive brands. The industry contributes significantly to the Thai economy and employs approximately half a million people. To address competitiveness of the Thai automotive industry, its supply chain is studied in this paper. Ten factors affecting the competitiveness of the Thai automotive supply chain are analysed through the fuzzy DEMATEL method. The method forms a structural model that categorises the causal or effect roles of the factors, and the degree at which they impact the supply chain. This study focuses on Tier 1 and Tier 2 manufacturers of the supply chain. The results reveal that there are three common causal factors and four common effects in both tiers. The other four factors play different roles in different tiers. Managerial implications relating to these factors are also discussed and certain recommendations proposed in order to enhance the competitiveness of the Thai automotive supply chain.     

On reliability estimation based on fuzzy lifetime data

Real lifetime data are never precise numbers but more or less non-precise, also called fuzzy. This kind of imprecision is connected with all measurement results of continuous variables, therefore also with time observations. Imprecision is different from errors and variability. Therefore estimation methods for reliability characteristics have to be adapted to the situation of fuzzy lifetimes in order to obtain realistic results.     

The t-information and its use in multivariate problems and time-series analysis

The second moment of recently introduced scalar inference function can be viewed as generalized Fisher information of the continuous probability distributions. In this paper we call it the t-information and give some possible applications of the new concept.     

R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions

Summary.  Despite its potential pitfalls, ecological inference is an unavoidable part of some quantitative settings, including US voting rights litigation. In such applications, the analyst will typically encounter two-way tables with more than two rows and columns. Although several ecological inference methods are currently available for 2×2 tables, there are fewer options for analysing general R × C tables, and virtually none that model counts as opposed to fractions. We propose a count R × C method that respects the bounds deterministically, that allows for complex relationships between internal cell quantities, that is easily extensible and that results from transparent assumptions. We study the method via simulation, and then apply it to an example that is drawn from the state of Texas relevant to recent redistricting litigation there.     

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Summary.  Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models , where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

从释义到推论——余英时《朱熹的历史世界》史料解读献疑

余英时先生《朱熹的历史世界》一书重构两宋（尤其是南宋）政坛面貌,旁征博引,新见迭出,深得学界好评。然而通读全书,作者对若干史料的运用,似不无令人生疑之处。书中史料处理的疑点。主要可分为：字词含义、语段脉络、史料性质、推论程序四类。     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.