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.     

内开槽螺纹波导冷腔色散特性模拟研究

根据螺纹波导中模式间的耦合方程组及冷腔色散方程进行了数值计算,分析了2个模式的相互耦合机理。基于圆柱形波导中线性极化TE11模式存在极化简并的特性,将线极化TE11脉冲作为1个探针,采用粒子模拟软件对该腔体进行了3维冷腔模拟;并将模拟结果与数值计算结果进行了比较,两者基本一致。通过计算机模拟得出了混合模式的电场等位线分布图,结合色散方程数值计算分析了内开槽螺纹几何特性变化对波导色散特性的影响规律。     

一个四层非均匀平面光波导的色散方程

借助 W.K.B 方法,参照四层均匀平面光波导的色散方程,提出了一个适用于计算四层非均匀平面光波导传播常数的色散方程。理论分析与数值计算表明,该方程不仅适用于具有任意折射率分布的四层非均匀波导,而且计算步骤简单,精度高。在0.6～1.55μm 波长范围内,用我们的公式与严格解析公式进行计算,其结果的最大差值<±1×10~(-4)。     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Bayesian density regression for discrete outcomes

We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.     

铁弗匈奴的族源、族称及其流散

铁弗匈奴是南匈奴去卑一支之后裔,因故逃归塞外之后,与迁居匈奴故地的鲜卑混居杂处而形成的一个新族体。其族称＂铁弗＂始于刘虎,源于北人谓＂胡父鲜卑母＂为＂铁弗＂。史书中有时称其为＂乌丸（桓）＂,是因拓跋魏将诸方杂人来附者谓之＂乌丸＂,至于与屠各之关系则是刘渊族源的衍生问题。夏政权灭亡之后,铁弗匈奴流散情况主要有三种：一是归入北魏;二是融入吐谷浑;三是继续保持＂刘＂姓活动在朔方地区。     

中国的贸易开放与工资收入差距:来自制造业细分行业的经验研究

运用中国制造业细分行业1993—2009年间的面板数据,研究了贸易开放对行业间工资收入差距的影响。研究结果显示,随着中国贸易开放的不断深入,行业间工资收入不平等呈现出逐步扩大的趋势,这与传统贸易理论对发展中国家相对工资差距的预测是不一致的。从要素密集度的层面来看,对于资本密集型行业来说,相对于其他行业,进出口总额扩大了行业间的收入不平等,而出口变量的影响不明显;对于技术密集型行业来说,相对于其他行业,进口与出口都扩大了行业间的收入不平等。     

New faces in new spaces in new places: Residential attainment among newly legalized immigrants in established,new, and minor destinations

Immigrants at the beginning of the twenty-first century are located in a more diverse set of metropolitan areas than at any point in U.S. history. Whether immigrants' residential prospects are helped or hindered in new versus established immigrant-receiving areas has been the subject of debate. Using multilevel models and data from the New Immigrant Survey (NIS), a nationally representative sample of newly legalized immigrants to the U.S., we move beyond aggregate-level analyses of residential segregation to specify the influence of destination type on individual-level immigrant residential outcomes. The findings indicate that immigrants in new and minor destinations are significantly more likely to live in tracts with relatively more non-Hispanic whites and relatively fewer immigrants and poor residents. These residential advantages persist net of individual-level controls but are largely accounted for by place-to-place differences in metropolitan composition and structure. Our exclusive focus on newly legalized immigrants means that our findings do not necessarily contradict the possibility of worse residential prospects in new areas of settlement, but rather qualifies it as not extending to the newly authorized population.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.