中国交通事故发生机制的空间计量分析

采用1997—2007年全国31个省份的面板数据,在城市化与交通事故发生之间关系分析的基础上,考察中国交通事故发生的空间相关性,并对影响交通事故次数和经济损失的因素进行了空间计量分析。结果发现:城市化水平的提高增加了交通事故发生率,家用车辆、人口密度和公路里程同样是交通事故发生的诱发因素,而发展公共交通则可以有效避免交通事故发生,道路配置水平的影响有待于进一步研究。     

A Bayesian model for ranking hazardous road sites

Summary.  Road safety has recently become a major concern in most modern societies. The identification of sites that are more dangerous than others (black spots) can help in better scheduling road safety policies. This paper proposes a methodology for ranking sites according to their level of hazard. The model is innovative in at least two respects. Firstly, it makes use of all relevant information per accident location, including the total number of accidents and the number of fatalities, as well as the number of slight and serious injuries. Secondly, the model includes the use of a cost function to rank the sites with respect to their total expected cost to society. Bayesian estimation for the model via a Markov chain Monte Carlo approach is proposed. Accident data from 519 intersections in Leuven (Belgium) are used to illustrate the methodology proposed. Furthermore, different cost functions are used to show the effect of the proposed method on the use of different costs per type of injury.     

An MM Algorithm for Constrained Estimation in a Road Safety Measure Modeling

We model the effect of a road safety measure on a set of target sites with a control area for each site, and we suppose that the accident data recorded at each site are classified in different mutually exclusive types. We adopt the before–after technique and we assume that at any one target site the total number of accidents recorded is multinomially distibuted between the periods and types of accidents. In this article, we propose a minorization–majorization (MM) algorithm for obtaining the constrained maximum likelihood estimates of the parameter vector. We compare it with a gradient projection–expectation maximization (GP-EM) algorithm, based on gradient projections. The performance of the algorithms is examined through a simulation study of road safety data.     

A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey

The analysis of traffic accident data is crucial to address numerous concerns, such as understanding contributing factors in an accident''s chain-of-events, identifying hotspots, and informing policy decisions about road safety management. The majority of statistical models employed for analyzing traffic accident data are logically count regression models (commonly Poisson regression) since a count – like the number of accidents – is used as the response. However, features of the observed data frequently do not make the Poisson distribution a tenable assumption. For example, observed data rarely demonstrate an equal mean and variance and often times possess excess zeros. Sometimes, data may have heterogeneous structure consisting of a mixture of populations, rather than a single population. In such data analyses, mixtures-of-Poisson-regression models can be used. In this study, the number of injuries resulting from casualties of traffic accidents registered by the General Directorate of Security (Turkey, 2005–2014) are modeled using a novel mixture distribution with two components: a Poisson and zero-truncated-Poisson distribution. Such a model differs from existing mixture models in literature where the components are either all Poisson distributions or all zero-truncated Poisson distributions. The proposed model is compared with the Poisson regression model via simulation and in the analysis of the traffic data.     

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.     

行驶里程数对环境、交通和能源的影响——基于外部性视角的省际面板数据研究

近年来,机动车污染日趋成为中国空气污染的重要来源.选取2006-2010年中国30个省、市、自治区的面板数据,运用单位根检验、协整检验和面板数据模型等方法来研究行驶里程数对环境、交通和能源的影响.结论认为公路交通氮氧化物排放量、汽油消耗量、交通事故数与公路里程数之间存在显著的相关关系,为将行驶里程数纳入车险费率改革、开发绿色低碳车险产品提供参考.     

A Well-Rounded Curriculum in Statistics

Reprinted by special permission of “The Iron Age”, this article describes how the statistical method used in control of product quality can be used as a technique for the control of accidents. Preliminary studies of this new approach indicate that it will aid materially in reducing accident rates, the authors declare.     

Bayesian updating of atmospheric dispersion after a nuclear accident

Summary.  We consider a Bayesian forecasting system to predict the dispersal of contamination on a large scale grid in the event of an accidental release of radioactivity. The statistical model is built on a physical model for atmospheric dispersion and transport called MATCH. Our spatiotemporal model is a dynamic linear model where the state parameters are the (essentially, deterministic) predictions of MATCH; the distributions of these are updated sequentially in the light of monitoring data. One of the distinguishing features of the model is that the number of these parameters is very large (typically several hundreds of thousands) and we discuss practical issues arising in its implementation as a realtime model. Our procedures have been checked against a variational approach which is used widely in the atmospheric sciences. The results of the model are applied to test data from a tracer experiment.     

Bayesian hierarchical models for linear networks

The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process. The heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second method, the segment-specific intensity is estimated. The homogeneous Poisson process is used to model accident data within grouped segments but heterogeneity across grouped segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo is used to estimate the unknown parameters in the models and the sensitivity to the choice of priors is assessed. The performance of the proposed models is evaluated by a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between models.     

Functional principal component analysis in age–period–cohort analysis of body mass index data by gender and ethnicity

In this paper, we propose a two-stage functional principal component analysis method in age–period–cohort (APC) analysis. The first stage of the method considers the age–period effect with the fitted values treated as an offset; and the second stage of the method considers the residual age–cohort effect conditional on the already estimated age-period effect. An APC version of the model in functional data analysis provides an improved fit to the data, especially when the data are sparse and irregularly spaced. We demonstrate the effectiveness of the proposed method using body mass index data stratified by gender and ethnicity.     

Factorial moment estimation for the bivariate generalized Waring distribution

In the context of accident theory, the bivariate generalized Waring distribution (Xekalaki, 1984) is known to offer the possibility of obtaining distinguishable estimates of the “contribution” of chance, risk exposure and proneness to an accident situation. In this paper an estimation procedure based on the first and second order factorial moments is discussed for fitting the distribution to data. Expressions for the asymptotic standard errors of the estimators of the distribution parameters as well as of the resulting estimators of the variance components that represent the roles of the above mentioned factors are given.     

Analysis of longitudinal data using a finite mixture model

This paper considers a finite mixture model for longitudinal data, which can be used to study the dependency of the shape of the respective follow-up curves on treatments or other influential factors and to classify these curves. An EM-algorithm to achieve the ml-estimate of the model is given. The potencies of the model are demonstrated using data of a clinical trial.     

Tests for stochastic ordering under biased sampling

In two-sample comparison problems it is often of interest to examine whether one distribution function majorises the other, that is, for the presence of stochastic ordering. This paper develops a nonparametric test for stochastic ordering from size-biased data, allowing the pattern of the size bias to differ between the two samples. The test is formulated in terms of a maximally selected local empirical likelihood statistic. A Gaussian multiplier bootstrap is devised to calibrate the test. Simulation results show that the proposed test outperforms an analogous Wald-type test, and that it provides substantially greater power over ignoring the size bias. The approach is illustrated using data on blood alcohol concentration of drivers involved in car accidents, where the size bias is due to drunker drivers being more likely to be involved in accidents. Further, younger drivers tend to be more affected by alcohol, so in making comparisons with older drivers the analysis is adjusted for differences in the patterns of size bias.     

Estimation in a random effects growth curve model

This paper considers estimation under the growth curve model of Potthoff and Roy (1964) with random effects. Estimation under a multivariate model is also considered. Estimation under incomplete data and estimation of random effects are also discussed. A numerical example of data on bulls is presented to illustrate these techniques.     

Understanding the 2001 UK census migration and commuting data: the effect of small cell adjustment and problems of comparison with 1991

Summary.  Origin–destination statistics have been produced from the last three UK censuses. The paper describes what is new about the 2001 census interaction data on migration and commuting, considers the disclosure control methods that were applied to cells containing small values and demonstrates the problems that are associated with making comparisons with 1991 data. The effect of small cell adjustment procedures on the interaction data sets is investigated by means of selective analyses at different spatial scales. Some recommendations are made in light of the problems that were manifest in 2001.     

Bayesian Survival Analysis of Head and Neck Cancer Data Using Lognormal Model

The paper considers a lognormal model for the survival times and obtains a Bayes solution by means of Gibbs sampler algorithm when the priors for the parameters are vague. The formulation given in the paper is mainly focused for censored data problems though it is equally well applicable for complete data scenarios as well. For the purpose of numerical illustration, we considered two real data sets on head and neck cancer patients when they have been treated using either radiotherapy or chemotherapy followed by radiotherapy. The paper not only compares the survival functions for the two therapies assuming a lognormal model but also provides a model compatibility study based on predictive simulation results so that the choice of lognormal model can be justified for the two data sets. The ease of our analysis as compared to an earlier approach is certainly an advantage.     

Nonparametric Test for Doubly Interval-Censored Failure Time Data

This paper considers comparison of discrete failure time distributions when the survival time of interest measures elapsed time between two related events and observations on the occurrences of both events could be interval-censored. This kind of data is often referred to as doubly interval-censored failure time data. If the occurrence of the first event defining the survival time can be exactly observed, the data are usually referred to as interval-censored data. For the comparison problem based on interval-censored failure time data, Sun (1996) proposed a nonparametric test procedure. In this paper we generalize the procedure given in Sun (1996) to doubly interval-censored data case and the generalized test is evaluated by simulations.     

Estimation of global temperature fields from scattered observations by a spherical-wavelet-based spatially adaptive method

Summary.  The paper considers the problem of estimating the entire temperature field for every location on the globe from scattered surface air temperatures observed by a network of weather-stations. Classical methods such as spherical harmonics and spherical smoothing splines are not efficient in representing data that have inherent multiscale structures. The paper presents an estimation method that can adapt to the multiscale characteristics of the data. The method is based on a spherical wavelet approach that has recently been developed for a multiscale representation and analysis of scattered data. Spatially adaptive estimators are obtained by coupling the spherical wavelets with different thresholding (selective reconstruction) techniques. These estimators are compared for their spatial adaptability and extrapolation performance by using the surface air temperature data.     

The analysis of some biological data

This paper considers the building of stochastic models and the related analysis of discrete data in two biological problems, The first arises from the reproduction of yeast cells, while the secondis concerned with the aggregation of nucleoli. Galton-Watson and aggregation models are constructed for the respective processes and their goodness of fit to the data tested