Winds from a Bayesian Hierarchical Model: Computation for Atmosphere-Ocean Research

Advances in computing power are allowing researchers to use Bayesian hierarchical models (BHM) on problems previously considered computationally infeasible. This article discusses the procedure of migrating a BHM from a workstation-class implementation to a massively parallel architecture, indicative of the current direction of advances in computing hardware. The parallel implementation is nearly 500 times larger than the workstation-class implementation from the data perspective. The BHM in question combines the information from a scatterometer on board a polar-orbiting satellite and the result of a numerical weather prediction model and produces an ensemble of high-resolution tropical surface wind fields with physically realistic variability at all spatial scales.     

Packing Densities and Simulated Tempering for Hard Core Gibbs Point Processes

Monotonicity and convergence properties of the intensity of hard core Gibbs point processes are investigated and compared to the closest packing density. For such processes simulated tempering is shown to be an efficient alternative to commonly used Markov chain Monte Carlo algorithms. Various spatial characteristics of the pure hard core process are studied based on samples obtained with the simulated tempering algorithm.     

N参数Ornstein-Uhlenbeck过程的点常返性

本文得到了Ｎ参数 Ｏｒｎｓｔｅｉｎ－Ｕｈｌｅｎｂｅｃｋ过程的点常返性准则，我们证明了：若 ｄ＜ ２Ｎ，则ｄ－维 ＯＵＰＮ，Ｘｚ是点常返的；若 ｄ ≥２Ｎ，则Ｘｚ以概率 １不击中点。     

Zero Expectile Processes and Bayesian Spatial Regression

We introduce new classes of stationary spatial processes with asymmetric, sub-Gaussian marginal distributions using the idea of expectiles. We derive theoretical properties of the proposed processes. Moreover, we use the proposed spatial processes to formulate a spatial regression model for point-referenced data where the spatially correlated errors have skewed marginal distribution. We introduce a Bayesian computational procedure for model fitting and inference for this class of spatial regression models. We compare the performance of the proposed method with the traditional Gaussian process-based spatial regression through simulation studies and by applying it to a dataset on air pollution in California.     

两指标点过程的停止及σ-域

本文给出两指标点过程的跳线刻划、停止方法,论述了跳线的可测性及点过程有关的σ-域的结构和特征。     

Detecting Features in Spatial Point Processes with Clutter via Local Indicators of Spatial Association

We consider the problem of detecting features of general shape in spatial point processes in the presence of substantial clutter. Our goal is to remove clutter from images where one or several features are present and have to be detected. We use a method based on local indicators of spatial association (LISA) functions, particularly on the development of a local version of the product density which is a second-order characteristic of spatial point processes. The classification method is built upon a stochastic version of the EM algorithm (SEM). This method can be applied without user input about the number or shapes of the regions. Our proposal, compared with the kth nearest-neighbor technique, is tested through simulated examples yielding high detection and low false-positive rates. Two real case studies of connective loose tissues in human organs and earthquakes are also presented.     

Cluster Algorithms for Spatial Point Processes with Symmetric and Shift Invariant Interactions

Abstract

In this article, Swendsen–Wang–Wolff algorithms are extended to simulate spatial point processes with symmetric and stationary interactions. Convergence of these algorithms is considered. Some further generalizations of the algorithms are discussed. The ideas presented in this article can also be useful in handling some large and complicated systems.     

An Interior Point Heuristic for the Hamiltonian Cycle Problem via Markov Decision Processes

We consider the Hamiltonian cycle problem embedded in a singularly perturbed Markov decision process (MDP). More specifically, we consider the HCP as an optimization problem over the space of long-run state-action frequencies induced by the MDP's stationary policies. We show that Hamiltonian cycles (if any) correspond to the global minima of a suitably constructed indefinite quadratic programming problem over the frequency space. We show that the above indefinite quadratic can be approximated by quadratic functions that are `nearly convex' and as such suitable for the application of logarithmic barrier methods. We develop an interior-point type algorithm that involves an arc elimination heuristic that appears to perform rather well in moderate size graphs. The approach has the potential for further improvements.     

An Asymptotic Formula of the Hellinger Transform for Multivariate Point Processes

Multivariate point processes are used in survival analysis in clinical research, in demography and econometrics, in reliability analysis, etc. The author and Yu. N. Linkov used the Hellinger transform in the investigation of properties of optimal estimates and statistical criteria. In this paper, we establish a formula of the rate function for probability measures corresponding to stochastic integrals of multivariate point processes and find an asymptotic formula of the Hellinger transform for such processes. We use a nontraditional representation of the Hellinger transform and the theory of large deviations.     

Continuity and Boundedness of Infinitely Divisible Processes: A Poisson Point Process Approach

Sufficient conditions for boundedness and continuity are obtained for stochastically continuous infinitely divisible processes, without Gaussian component, {Y(t),t T}, where T is a compact metric space or pseudo-metric space. Such processes have a version given by Y(t)=X(t)+b(t),tT where b is a deterministic drift function and

中国经济随机前沿模型效率的贝叶斯统计推断

基于扩展的随机生产前沿模型,研究了区域生产效率的差异和其影响因素的作用效果,应用贝叶斯统计方法对中国各省份2010-2017的年度数据(不包含港澳台地区,下同)进行了实证研究.研究发现:生产效率总体呈逐渐下降的趋势,地区间生产效率有一定的差异,高等教育规模对生产效率具有显著的直接影响.人力资本能有效促进东部和中部地区的经济增长,西部地区主要依靠资本促进经济增长.环境污染对中部地区的经济增长具有一定的负向作用.     

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A Bayesian semiparametric procedure for confirmatory factor analysis model is proposed to address the heterogeneity of the multivariate responses. The approach relies on the use of a prior over the space of mixing distributions with finite components. Blocked Gibbs sampler is implemented to cope with the posterior analysis. For model comparison, themeasure and Bayes factor are developed. A generalized weighted Chinese restaurant algorithm is suggested to compute the likelihood of data. Empirical results are presented to illustrate the effectiveness of the methodologies.     

非锥映射的不动点指数计算及其应用

本文应用锥理论获得了一类不是锥映射的非线性算子不动点指数计算的若干结果,并将所得结果应用于非线性椭圆型偏微分方程边值问题.     

Bayesian and Maximum Likelihood Estimation for Gaussian Processes on an Incomplete Lattice

This article proposes a new approach for Bayesian and maximum likelihood parameter estimation for stationary Gaussian processes observed on a large lattice with missing values. We propose a Markov chain Monte Carlo approach for Bayesian inference, and a Monte Carlo expectation-maximization algorithm for maximum likelihood inference. Our approach uses data augmentation and circulant embedding of the covariance matrix, and provides likelihood-based inference for the parameters and the missing data. Using simulated data and an application to satellite sea surface temperatures in the Pacific Ocean, we show that our method provides accurate inference on lattices of sizes up to 512 × 512, and is competitive with two popular methods: composite likelihood and spectral approximations.     

A Method for Testing Nested Point Null Hypotheses Using Multiple Bayes Factor

As a flexible Bayesian test criterion for nested point null hypotheses, asymmetric and multiple Bayes factors are introduced in the form of a modified Savage-Dickey density ratio. This leads to a simple method for obtaining pairwise comparisons of hypotheses in a statistical experiment with a partition on the parameter space. The method is derived from the fact that in general, the asymmetric Bayes factor can be written as the product of the Savage-Dickey ratio and a correction factor where both terms are easily estimated by means of posterior simulation. Analyses of a censored data problem and a serial correlation problem are illustrated for the method. For these cases, the method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required tests.     

Estimation of Drift Parameter and Change Point for Switching Fractional Diffusion Processes

We study the asymptotic distribution of the maximum likelihood estimator for the drift parameter and the change point for fractional diffusion processes as the noise intensity tends to zero.     

A General Class of Change Point and Change Curve Modeling for Life Time Data

Change point hazard rate models arise in many life time data analysis, for example, in studying times until the undesirable side effects occur in clinical trials. In this paper we propose a general class of change point hazard model for survival data. This class includes and extends different types of change point models for survival data, e.g. cure rate model and lag model. Most classical approach develops estimates of model parameters, with particular interest in change point parameter and often the whole hazard function, but exclusively in terms of asymptotic properties. We propose a Bayesian approach, avoiding asymptotics and provide inference conditional upon the observed data. The proposed Bayesian models are fitted using Markov chain Monte Carlo method. We illustrate our proposed methodology with an application to modeling life times of the printed circuit board.     

Banach空间中Lipschitz伪压缩映射的近似不动点序列及其收敛定理

研究了Lipschitz伪压缩映射的黏滞迭代方法.设E为一致光滑Bannach空间,K为E的闭凸子集,TK→K为Lipschitz伪压缩映射且其不动点集F(T)非空,f为K上的压缩映射且t∈(0,1).若黏滞迭代路径{xt},xt=(1-t)f(xt) tTxt且对任意初始向量x1∈K,迭代序列{xn}定义为xn 1=λnθnf(xn) [1-λn(1 θn)]xn λnTxn,则当t→1-和n→∞时,{xt}和{xn}都强收敛于T的不动点,同时该不动点还是一类变分不等式的解.