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.     

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.     

Residential mobility and the spatial dispersion of personal networks: Effects on social support

Using a representative national sample of personal networks, this article explores how the spatial dispersion of networks, residential mobility and social support are linked. Three issues will be addressed here. Firstly, how is the spatial dispersion of personal networks related to individuals’ social characteristics, network composition and residential mobility? Secondly, how do the spatial dispersion of networks, residential mobility and their combined effect influence the number and (thirdly) the structure of emotional support ties? Results showed that the extent of the support was affected neither by the geographical distribution of the networks nor by residential mobility. Living far from one's birthplace, however, exerted two distinct, and opposite effects on the support network structure. On the one hand, mobility led to high spatial dispersion of personal contacts, which in turn favored a sparsely knit network centered around the mobile individual. On the other hand, by controlling for the effect of distance between the contacts, we found that individuals that cited long-distance ties tended to be part of more transitive support networks than those that cited local ties. We interpreted the latter effect as evidence that transitive ties may survive greater spatial distances than intransitive ones. These findings are discussed in view of spatial mobility and social network research.     

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.     

Analysis of robust design experiments with time-dependent ordinal response characteristics: a quality improvement study from the horticulture industry

An approach to the analysis of time-dependent ordinal quality score data from robust design experiments is developed and applied to an experiment from commercial horticultural research, using concepts of product robustness and longevity that are familiar to analysts in engineering research. A two-stage analysis is used to develop models describing the effects of a number of experimental treatments on the rate of post-sales product quality decline. The first stage uses a polynomial function on a transformed scale to approximate the quality decline for an individual experimental unit using derived coefficients and the second stage uses a joint mean and dispersion model to investigate the effects of the experimental treatments on these derived coefficients. The approach, developed specifically for an application in horticulture, is exemplified with data from a trial testing ornamental plants that are subjected to a range of treatments during production and home-life. The results of the analysis show how a number of control and noise factors affect the rate of post-production quality decline. Although the model is used to analyse quality data from a trial on ornamental plants, the approach developed is expected to be more generally applicable to a wide range of other complex production systems.