Modeling and Anomalous Cluster Detection for Point Processes Using Process Convolutions

We present a model using process convolutions, which describes spatial and temporal variations of the intensity of events that occur at random geographical locations. An inhomogeneous Poisson process is used to model the intensity over a spatial region with multiplicative spatial and temporal covariate effects. Temporal variation in the structure of the intensity is obtained by employing a time-varying process for the convolution. Use of a compactly supported kernel in the convolution improves the computational efficiency. Additionally, anomalous cluster detection in the event rates is developed based on exceedance probabilities. The methods are demonstrated on data of major crimes in Cincinnati during 2006. Supplementary materials for this article are available online.     

Point process diagnostics based on weighted second-order statistics and their asymptotic properties

A new approach for point process diagnostics is presented. The method is based on extending second-order statistics for point processes by weighting each point by the inverse of the conditional intensity function at the point’s location. The result is generalized versions of the spectral density, R/S statistic, correlation integral and K-function, which can be used to test the fit of a complex point process model with an arbitrary conditional intensity function, rather than a stationary Poisson model. Asymptotic properties of these generalized second-order statistics are derived, using an approach based on martingale theory.     

A Bayesian Sampling Approach to Regression Model Checking

A necessary step in any regression analysis is checking the fit of the model to the data. Graphical methods are often employed to allow visualization of features that the data should exhibit if the model holds. Judging whether such features are present or absent in any particular diagnostic plot can be problematic. In this article I take a Bayesian approach to aid in this task. The “unusualness” of some data with respect to a model can be assessed using the predictive distribution of the data under the model; an alternative is to use the posterior predictive distribution. Both approaches can be given a sampling interpretation that can then be used to enhance regression diagnostic plots such as marginal model plots.     

Graphical Exploratory Analysis of Survival Data

Abstract

We present dynamic and static graphs for exploratory analysis of survival data. These graphs are based on a smooth semiparametric estimate of the survival probability as a function of time and a covariate. We overlay a contour plot of the conditional survival distribution on a scatterplot of time and covariate. This is augmented by plots of the estimated survival function at particular covariate values and the receiver operating characteristic curve at particular time points. In our XLisp-Stat implementation these plots are linked and the time and covariate values for the augmenting plots can be varied dynamically. The methods are illustrated on data from a clinical study of liver disease.     

变化底部对非线性表面波的影响

考虑表面张力的作用,研究了不可压缩、无粘性流体流过变化壁面时的共振流动,分析了不同的底部壁面变化对非线性表面波的影响．在导出非线性表面波遵循的fKdV方程后,利用拟谱方法进行数值模拟,用Matlab软件绘制瀑布图,由此得出结论:上凸底部上的波可以看成是向前凸台阶和向后凸台阶分别向前后散射发展的结果,二者不发生相互作用;下凹壁面的波形是向前凹台阶和向后凹台阶相互作用的结果;某些组合式底部的波形是上凸和下凹相互作用的结果．     

A spatio-temporal dynamic regression model for extreme wind speeds

This paper presents a hierarchical regression type model for analyzing the dependency of sample extremes on time, space and a covariate effect. The model is based on the assumption that the observations follow independently a generalized extreme value distribution given location, scale and shape parameters. Then a multivariate spatial process is considered to accommodate the association and spatial correlation in the distribution parameters. The mean of the process incorporates the underlying dynamics which is elaborated on the lower stage of hierarchy. Finally, three spatio-temporal dynamic linear models drive independently this mean function to take the variations in the parameters separately into account. In a Bayesian setting, the model structure leads to parallel implementation of the Markov chain Monte Carlo algorithm in a sense that it is less time consuming. Our methodology is applied to the monthly maxima of wind speed with temperature as a covariate for which the relationship is expressed in terms of a penalized spline regression model. The comparison of the proposed model with several simpler ones suggests considerable improvements in wind speed analysis.     

Some Q-Q Probability Plots to Test Spherical and Elliptical Symmetry

Abstract

This article proposes some probability plots are proposed to test spherical and elliptical symmetry in terms of some invariant statistics under orthogonal transformations. Some correlation coefficients as numerical measures of detecting deviation from spherical or elliptical symmetry are recommended, and the empirical percentiles of these correlation coefficients are calculated by simulation. The simulation results for data sets from 12 different populations show that the new plots are useful for testing spherical and elliptical symmetry. Some discussion is given also.     

Simulated annealing and object point processes: Tools for analysis of spatial patterns

This paper introduces a three-dimensional object point process—the Bisous model—that can be used as a prior for three-dimensional spatial pattern analysis. Maximization of likelihood or penalized-likelihood functions based on this model requires global optimization techniques, such as the simulated annealing algorithm. Theoretical properties of the model are discussed and the convergence of the proposed optimization method is proved. Finally, a simulation study is presented.     

Discovering metamodels’ quality-of-fit for simulation via graphical techniques

Metamodels are used in many disciplines to replace simulation models of complex multivariate systems. To discover metamodels ‘quality-of-fit’ for simulation, simple information returned by average-based statistics, such as root-mean-square error RMSE, are often used. The sample of points used in determining these averages is restricted in size, especially for simulation models of complex multivariate systems. Obviously, decisions made based on average values can be misleading when the sample size is not adequate, and contributions made by each individual data point in such samples need to be examined. This paper presents methods that can be used to discover metamodels quality-of-fit graphically by means of two-dimensional plots. Three plot types are presented; these are the so-called circle plots, marksman plots, and ordinal plots. Such plots can be used to facilitate visual inspection of the effect on metamodel accuracy of each individual point in the data sample used for metamodel validation. The proposed methods can be used to complement quantitative validation statistics; in particular, for situations where there is not enough validation data or the validation data is too expensive to generate.     

Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma law

This paper discusses practical Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma laws. Estimation is based on a parameterization which is derived from the Rosiński representation, and has the advantage of being a non-centered parameterization. The parameterization is based on a marked point process, living on the positive real line, with uniformly distributed marks. We define a Markov chain Monte Carlo (MCMC) scheme which enables multiple updates of the latent point process, and generalizes single updating algorithm used earlier. At each MCMC draw more than one point is added or deleted from the latent point process. This is particularly useful for high intensity processes. Furthermore, the article deals with superposition models, where it discuss how the identifiability problem inherent in the superposition model may be avoided by the use of a Markov prior. Finally, applications to simulated data as well as exchange rate data are discussed.     

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

This article presents individual conditional expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. Classical partial dependence plots (PDPs) help visualize the average partial relationship between the predicted response and one or more features. In the presence of substantial interaction effects, the partial response relationship can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate the complexity of the modeled relationship. Accordingly, ICE plots refine the PDP by graphing the functional relationship between the predicted response and the feature for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate, suggesting where and to what extent heterogeneities might exist. In addition to providing a plotting suite for exploratory analysis, we include a visual test for additive structure in the data-generating model. Through simulated examples and real datasets, we demonstrate how ICE plots can shed light on estimated models in ways PDPs cannot. Procedures outlined are available in the R package ICEbox.     

Cox Point Processes Driven by Ornstein–Uhlenbeck Type Processes

The paper is devoted to the development of Cox point processes driven by nonnegative processes of Ornstein–Uhlenbeck (OU) type. Starting with multivariate temporal processes we develop formula for the cross pair correlation function. Further filtering problem is studied by means of two different approaches, either with discretization in time or through the point process densities with respect to the Poisson process. The first approach is described mainly analytically while in the second case we obtain numerical solution by means of MCMC. The Metropolis–Hastings birth–death chain for filtering can be also used when estimating the parameters of the model. In the second part we try to develop spatial and spatio-temporal Cox point processes driven by a stationary OU process. The generating functional of the point process is derived which enables evaluation of basic characteristics. Finally a simulation algorithm is given and applied.      

Interaction Processes for Unions of Facets,the Asymptotic Behaviour with Increasing Intensity

In the series of models with interacting particles in stochastic geometry, a further contribution presents the facet process which is defined in arbitrary Euclidean dimension. In 2D, 3D specially it is a process of interacting segments, flat surfaces, respectively. Its investigation is based on the theory of functionals of finite spatial point processes given by a density with respect to a Poisson process. The methodology based on L ₂ expansion of the covariance of functionals of Poisson process is developed for U-statistics of facet intersections which are building blocks of the model. The importance of the concept of correlation functions of arbitrary order is emphasized. Some basic properties of facet processes, such as local stability and repulsivness are shown and a standard simulation algorithm mentioned. Further the situation when the intensity of the process tends to infinity is studied. In the case of Poisson processes a central limit theorem follows from recent results of Wiener-Ito theory. In the case of non-Poisson processes we restrict to models with finitely many orientations. Detailed analysis of correlation functions exhibits various asymptotics for different combination of U-statistics and submodels of the facet process.     

Approximate Bayesian Computation and Model Assessment for Repulsive Spatial Point Processes

In many applications involving spatial point patterns, we find evidence of inhibition or repulsion. The most commonly used class of models for such settings are the Gibbs point processes. A recent alternative, at least to the statistical community, is the determinantal point process. Here, we examine model fitting and inference for both of these classes of processes in a Bayesian framework. While usual MCMC model fitting can be available, the algorithms are complex and are not always well behaved. We propose using approximate Bayesian computation (ABC) for such fitting. This approach becomes attractive because, though likelihoods are very challenging to work with for these processes, generation of realizations given parameter values is relatively straightforward. As a result, the ABC fitting approach is well-suited for these models. In addition, such simulation makes them well-suited for posterior predictive inference as well as for model assessment. We provide details for all of the above along with some simulation investigation and an illustrative analysis of a point pattern of tree data exhibiting repulsion. R code and datasets are included in the supplementary material.     

Influence diagnostics for the proportional hazards model

Influence diagnostics based on Cook's curvature diagnostic (1986) are developed for the proportional hazards model. Three perturbation schemes are considered: perturbation of the likelihood, perturbation of the censoring information and perturbation of covariate values.     

Additive–multiplicative hazards model with current status data

The additive–multiplicative hazards (AMH) regression model specifies an additive and multiplicative form on the hazard function for the counting process associated with a multidimensional covariate process, which contains the Cox proportional hazards model and the additive hazards model as its special cases. In this paper, we study the AMH model with current status data, where the cumulative hazard hazard function is assumed to be nonparametric and is estimated using B-splines with monotonicity constraint on the functional, while a simultaneous sieve maximum likelihood estimation is proposed to estimate regression parameters. The proposed estimator for the parameter vector is shown to be asymptotically normal and semiparametric efficient. The B-splines estimator of the functional of the cumulative hazard function is shown to achieve the optimal nonparametric rate of convergence. A simulation study is conducted to examine the finite sample performance of the proposed estimators and algorithm, and a real data example is presented for illustration.     

Identification and estimation of superposed Neyman–Scott spatial cluster processes

This paper proposes an estimation method for superposed spatial point patterns of Neyman–Scott cluster processes of different distance scales and cluster sizes. Unlike the ordinary single Neyman–Scott model, the superposed process of Neyman–Scott models is not identified solely by the second-order moment property of the process. To solve the identification problem, we use the nearest neighbor distance property in addition to the second-order moment property. In the present procedure, we combine an inhomogeneous Poisson likelihood based on the Palm intensity with another likelihood function based on the nearest neighbor property. The derivative of the nearest neighbor distance function is regarded as the intensity function of the rotation invariant inhomogeneous Poisson point process. The present estimation procedure is applied to two sets of ecological location data.     

协变量区间删失时线性模型的参数估计

本文研究了当协变量为区间数据时的线性模型，通过构造区间数据变量的条件均值，得到了回归参数的估计，当协变量的分布已知时，证明了估计的无偏性与强相合性．时协变量的分布未知的情形也作了讨论．文中还作了若干模拟计算，从模拟的结果不难发现，利用本文提出的方法所获得的估计简便且具有较高的精度．     

基于改进NSGA-II算法的多级服务设施备用覆盖选址决策模型

为了应对跨区域突发事件过程中受灾点服务差异化需求的问题,建立了应急储备设施点的多级备用覆盖选址决策模型,即一个需求点由多个应急设施提供不同质量水平的服务,并考虑设施繁忙状态下由其他设施点提供服务的状况,使模型更加符合实际应用。首次通过设计分段的染色体编码方式改进NSGA-II算法提升运算效率以更好地解决多目标选址决策问题,将改进方法下得到的Pareto解分布与NSGA-II算法下的仿真结果进行对比分析,结合设施点的部署策略得到不同的空间布局方案。证明了模型的可行性及改进NSGA-II算法在解决设施点多目标选址决策问题时的有效性。