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基于测度空间变分分析的聚类方法AI 解读

We formulate clustering as a minimisation problem in the space of measures by modelling the cluster centres as a Poisson process with unknown intensity function. We derive a Ward‐type clustering criterion which, under the Poisson assumption, can easily be evaluated explicitly in terms of the intensity function. We show that asymptotically, i.e. for increasing total intensity, the optimal intensity function is proportional to a dimension‐dependent power of the density of the…

空间相关数据的客观贝叶斯分析AI 解读

Spatially varying phenomena are often modeled using Gaussian random fields, specified by their mean function and covariance function. The spatial correlation structure of these models is commonly specified to be of a certain form (e.g., spherical, power exponential, rational quadratic, or Matérn) with a small number of unknown parameters. We consider objective Bayesian analysis of such spatial models, when the mean function of the Gaussian random field is specified as in a…

利用二重条件自回归模型改进死亡率估计的双变量贝叶斯方法AI 解读

A bivariate Bayes method is proposed for estimating the mortality rates of a single disease for a given population, using additional information from a second disease. The information on the two diseases is assumed to be from the same population groups or areas. The joint frequencies of deaths for the two diseases for given populations are assumed to have a bivariate Poisson distribution with joint means proportional to the population sizes. The relationship between the…

摘要暂缺

利用局部似然追踪断裂线与边界AI 解读

Summary We suggest locally parametric methods for estimating curves, such as boundaries of density supports or fault lines in response surfaces, in a variety of spatial problems. The methods are based on spatial approximations to the local likelihood that the curve passes through a given point in the plane, as a function of that point. The local likelihood might be a regular likelihood computed locally, with kernel weights (e.g. in the case of support boundary estimation) or…

随机块结构的估计与预测AI 解读

A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned into several unobserved (latent) classes and that the probability distribution of the relation between two vertices depends only on the classes to which they belong. A Bayesian estimator based on Gibbs sampling is proposed. The basic model is not identified, because class labels are arbitrary. The…

通过马尔可夫链蒙特卡洛随机逼近计算空间模型的最大似然估计AI 解读

Summary We propose a two-stage algorithm for computing maximum likelihood estimates for a class of spatial models. The algorithm combines Markov chain Monte Carlo methods such as the Metropolis–Hastings–Green algorithm and the Gibbs sampler, and stochastic approximation methods such as the off-line average and adaptive search direction. A new criterion is built into the algorithm so stopping is automatic once the desired precision has been set. Simulation studies and…

标记点过程统计量的矩估计AI 解读

Summary In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and we establish their L 2-consistency. The variance estimator in particular can be used for the construction of confidence intervals for estimated…

摘要暂缺

按空间细分地区构建房地产指数AI 解读

A spatial–temporal Markov random-field model is used to produce indices for residential real estate from repeat home sale data. A set of regions is represented by a graph in which neighboring regions are linked. This graph, repeated consecutively a number of times, with each region linked to the same region at adjacent times, defines a spatial–temporal graph that connects regions over space and time in which each node represents a region at a particular time. An index is…

利用金融与经济变量预测股票收益的非线性可预测性AI 解读

In a recent article by Qi, neural networks trained by Bayesian regularization were used to predict excess returns on the S&P 500. The article concluded that the switching portfolio based on the recursive neural-network forecasts generates higher accumulated wealth with lower risks than that based on linear regression. Unfortunately, attempts to replicate the results were unsuccessful. Replicated results using the same software, approach and data detailed by Qi indicate that,…

空间非齐次泊松过程的拟合优度检验AI 解读

We propose an extension of the classical complete spatial randomness tests to nonstationary Poisson spatial processes. The method consists of first performing the classical tests locally and then grouping the local results into a global test. The global test is a test for nonstationary Poisson process assumption, whereas the local tests can be used in an exploratory way to decide whether the observed process is locally regular or clustered or if we do not reject the…

非平稳时间序列的描述性计量方法及实证示例AI 解读

Abstract Recent work by the author on methods of spatial density analysis for time series data with stochastic trends is reviewed. The methods are extended to include processes with deterministic trends, formulae for the mean spatial density are given, and the limits of sample moments of non‐stationary data are shown to take the form of moments with respect to the underlying spatial density, analogous to population moments of a stationary process. The methods are illustrated…

利用小波方法检验未知形式的序列相关AI 解读

A wavelet-based consistent test for serial correlation of unknown form is proposed. As a spatially adaptive estimation method, wavelets can effectively detect local features such as peaks and spikes in a spectral density, which can arise as a result of strong autocorrelation or seasonal or business cycle periodicities in economic and financial time series. The proposed test statistic is constructed by comparing a wavelet-based spectral density estimator and the null spectral…

点源附近疾病风险的贝叶斯建模AI 解读

Recently there has been increased interest, from both the media and the public, in the question, “Is there an excess of disease risk close to a prespecified point source?” To address this question, routinely available public health data may be analyzed. In the United Kingdom, as in many countries, health data and the associated population data that are required for comparison, are available as aggregated counts. In this article we propose to analyze such data using a…

慢性阻塞性肺疾病死亡率的贝叶斯分析与制图AI 解读

This article summarizes our research on estimation of age-specific and age-adjusted mortality rates for chronic obstructive pulmonary disease (COPD) for white males. Our objectives are more precise and informative displays (than previously available) of geographic variation of the age-specific mortality rates for COPD, and investigation of the relationships between the geographic variation in mortality rates and the corresponding variation in selected covariates. For a given…

不同空间分辨率健康与暴露数据的空间泊松回归AI 解读

Ecological regression studies are widely used to examine relationships between disease rates for small geographical areas and exposure to environmental risk factors. The raw data for such studies, including disease cases, environmental pollution concentrations, and the reference population at risk, are typically measured at various levels of spatial aggregation but are accumulated to a common geographical scale to facilitate statistical analysis. In this traditional…

家庭内肺炎链球菌携带传播：二元纵向数据的潜在马尔可夫过程模型AI 解读

We present a Bayesian data augmentation model to estimate acquisition and clearance rates of carriage of Streptococcus pneumoniae (Pnc) bacteria. The panel observation data comprise 10 measurements of Pnc carriage (carrier/noncarrier of the bacteria) in all members of 97 families with young children over a period of 2 years. Using natural conditional independence assumptions, a transmission model is constructed for the unobserved dependent binary processes of the augmented…

模糊化生成的非可分时空模型AI 解读

Summary Statistical space–time modelling has traditionally been concerned with separable covariance functions, meaning that the covariance function is a product of a purely temporal function and a purely spatial function. We draw attention to a physical dispersion model which could model phenomena such as the spread of an air pollutant. We show that this model has a non-separable covariance function. The model is well suited to a wide range of realistic problems which will…

空间边界不对齐数据的完全模型化方法AI 解读

We consider inference using multivariate data that are spatially misaligned; that is, involving variables (typically counts or rates) that are aggregated over differing sets of regional boundaries. Geographic information systems enable the simultaneous display of such datasets, but their current capabilities are essentially only descriptive, not inferential. We describe a hierarchical modeling approach that provides a natural solution to this problem through its ability to…

互联网流量数据AI 解读

Internet engineering and management depend on an understanding of the characteristics of network traffic. Statistical models are needed that can generate traffic that mimics closely the observed behavior on live Internet wires. Models can be used on their own for some tasks and combined with network simulators for others. But the challenge of model development is immense. Internet traffic data are ferocious. Their statistical properties are complex, databases are very large,…

空间数据插值：克里金法的若干理论AI 解读

1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- Exercises.- 1.3 Hilbert spaces and prediction.- Exercises.- 1.4 An example of a poor BLP.- Exercises.- 1.5 Best linear unbiased prediction.- Exercises.- 1.6 Some recurring themes.- The Matern model.- BLPs and BLUPs.- Inference for differentiable random fields.- Nested models are not tenable.- 1.7 Summary of practical suggestions.- 2 Properties of Random Fields.- 2.1 Preliminaries.- Stationarity.-…

空间错位数据的完全模型化分析方法AI 解读

We consider inference using multivariate data that are spatially misaligned; that is, involving variables (typically counts or rates) that are aggregated over differing sets of regional boundaries. Geographic information systems enable the simultaneous display of such datasets, but their current capabilities are essentially only descriptive, not inferential. We describe a hierarchical modeling approach that provides a natural solution to this problem through its ability to…

点过程强度函数中极点的估计AI 解读

Motivated by spatial data on earthquake epicentres, we consider the problem of estimating properties of poles in point-process intensity functions. Our methods are semiparametric, requiring only 'asymptotic' models for the intensity. They produce estimates of the locations and strengths of poles. Strength is expressed in terms of an exponent of regular variation, and is simply related to the correlation dimension of the underlying point process. It is argued that existing…