Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

中国内地与香港经济周期协同性的门限性质检验——兼论两地经济周期协同性中的美国因素

运用M arkov区制转移模型,对中国内地与香港经济周期协同性的门限性质以及美国对两地经济周期协同性的影响进行分析。结果显示:一方面,两地经济周期的协同性依赖于区制状态的"门限性质",即在不同的经济周期区制内呈现出不同的协同性水平。另一方面,两地经济周期的协同程度隐含了美国作为两地之间经济冲击的传递渠道以及两地共同的外部冲击源的影响。当剥离美国经济的影响之后,两地经济周期的协同程度较为微弱。因此,促进两地经济周期的长期趋同,应科学甄别和合理利用美国经济对两地经济周期协同性的影响。     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

An Application of Markov Chain Analysis to the Game of Squash*

If the score in a squash game is tied late in the game, one player has a choice of how many additional points (from a prespecified set of possibilities) are to be played to determine the winner. This paper constructs a Markov chain model of the situation and solves for the optimal strategy. Expressions for the optimal strategy are obtained with a symbolic algebra computer package. Results are given for both international and American scoring systems. The model and analysis are very suitable for educational purposes. The resulting Markov chain is small enough that it can be easily presented in a classroom setting, yet the model is sufficiently complex that algebraic manipulation is nearly hopeless. The final results illustrate the power of the combination of mathematical and computer modeling applied to a problem of practical interest.     

从学生误译看翻译中逻辑思维转换的意义

由于所处的地理环境和文化传统不同,英语民族和汉语民族在逻辑思维方式上存在不少差异。在英汉翻译过程中,从理解原文到组织译文,英汉两种思维模式很可能会互相干扰。根深蒂固的母语思维可能影响译者对原文的理解,同时英语思维定式也会对译文的组织产生干扰。即是说,逻辑思维转换与运用的正确与否是导致误译的主要原因之一,能否在英汉两种思维模式之间顺利转换是翻译成功的重要因素。     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data

In this paper, we consider parametric Bayesian inference for stochastic differential equations driven by a pure‐jump stable Lévy process, which is observed at high frequency. In most cases of practical interest, the likelihood function is not available; hence, we use a quasi‐likelihood and place an associated prior on the unknown parameters. It is shown under regularity conditions that there is a Bernstein–von Mises theorem associated to the posterior. We then develop a Markov chain Monte Carlo algorithm for Bayesian inference, and assisted with theoretical results, we show how to scale Metropolis–Hastings proposals when the frequency of the data grows, in order to prevent the acceptance ratio from going to zero in the large data limit. Our algorithm is presented on numerical examples that help verify our theoretical findings.     

Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial datasets, it is shown that the inverse gamma and generalized inverse gamma processes can greatly outperform the commonly used log normal volatility processes with Student’s t errors or jumps in the mean equation.     

关键事件技术及其应用——以制造商转换行为的研究为例

作为一种定性研究方法,关键事件技术特别适用于理论发展和管理有效性等研究目的。目前,该方法被广泛应用于教育、管理、零售、服务接触以及关系管理等领域进行探索性研究,其信度和效度也一再得到检验和证明。本文首先介绍了关键事件技术,并将其运用到制造商转换行为的研究之中。研究表明:价格、产品质量、物流能力、公司采购战略、服务质量是影响制造商转换的最为重要的原因。而且,不同的转换原因对制造商的满意度有不同的影响。在进一步分析的基础上,本文还得出85%的制造商转换属于复杂转换以及关系对顾客保留的影响可能与供应商所能提供的顾客价值有关等等富有意义的结论。     

具有延误休假的一个基本可修系统的可靠性分析

本文讨论了由两个同型部件和一个修理工组成的可修系统。假定修理工空闲时进行延误休假。部件的修理时间和修理工的假期均服从一般分布，休假前的延误期为指数分布。我们求得了这个系统的可用度、可靠度和故障频度。作为特例，我们还讨论了修理工进行不延误休假的情况。