Air Nicotine and Saliva Cotinine as Indicators of Workplace Passive Smoking Exposure and Risk1

We model nicotine from environmental tobacco smoke (ETS) in office air and salivary cotinine in nonsmoking U.S. workers. We estimate that: an average salivary cotinine level of 0.4 ng/ml corresponds to an increased lifetime mortality risk of 1/1000 for lung cancer, and 1/100 for heart disease; >95% of ETS-exposed office workers exceed OSHA's significant risk level for heart disease mortality, and 60% exceed significant risk for lung cancer mortality; 4000 heart disease deaths and 400 lung cancer deaths occur annually among office workers from passive smoking in the workplace, at the current 28% prevalence of unrestricted smoking in the office workplace.     

An investigation of the spatial correlations for relative purchasing power in Baden–Württemberg

The relative purchasing power—i.e., the purchasing power per inhabitant—is one of the key characteristics for businesses deciding on site selection. Apart from that it also plays a major role in regional planning, pricing policy and market research. In this study we investigate the spatial correlations for relative purchasing power of the townships in Baden–Württemberg. In particular, changes in relative purchasing power are analysed for three different time intervals, 1987–1993, 1993–1998 and 1998–2004, by means of distance-dependent characteristics like the mark–correlation function, the Simpson indices α(r) and β(r) and by tests on random labelling. It is shown that there are positive correlations for small distances between different townships but that these positive correlations become weaker over the years until they are almost nonexistent (in the sense that hypotheses of random labelling are no longer rejected). A conclusion from this loss of spatial correlations with time is that the relative purchasing power might become more and more purely random. This means that the relative purchasing power in a township is less and less influenced by the relative purchasing power of townships nearby. We further analysed these changes in the Bodensee–Oberschwaben and Stuttgart regions to compare the development of the relative purchasing power in both urban and rural environments. This analysis was done in close cooperation with W. Brachat–Schwarz and W. Walla of the Statistical Office of Baden–Württemberg. S.E. is supported by a grant of the graduate college 1100 at Ulm University.     

多Agent的股票市场建模中的技术问题分析

传统金融理论一直强调以线性、均衡的思维、标准计量型优化求解为主要特征,但难以捕捉市场中投资者复杂的学习行为及市场的动力行为,也难以揭示新兴市场的运行规律,而将多Agent技术引入到金融市场研究中,可以放开理性、均衡思想的束缚,并且化解数学模型对假设条件的苛刻要求.基于多Agent的股票市场建模研究作为新兴的领域,虽然在过去的二十多年中取得了显著的研究成果,但是目前仍然面临着对Agent抽象、分类、进化和结构模型设计等诸多急需解决的问题,根据股票市场的结构特征和投资者认知特征,借助复杂自适应系统的方法论和借鉴行为金融学的研究成果将有助于对此类问题的解决.     

An example of a two-part latent growth curve model for semicontinuous outcomes in the health sciences

A new method of modeling coronary artery calcium (CAC) is needed in order to properly understand the probability of onset and growth of CAC. CAC remains a controversial indicator of cardiovascular disease (CVD) risk, but this may be due to ill-equipped methods of specifying CAC during the analysis phase of studies reporting an analysis where CAC is the primary outcome. The modern method of two-part latent growth modeling may represent a strong alternative to the myriad of existing methods for modeling CAC. We provide a brief overview of existing methods of analysis used for CAC before introducing the general latent growth curve model, how it extends into a two-part (semicontinuous) growth model, and how the ubiquitous problem of missing data can be effectively handled. We then present an example of how to model CAC using this framework. We demonstrate that utilizing this type of modeling strategy can result in traditional predictors of CAC (e.g. age, gender, and high-density lipoprotein cholesterol), exerting a different impact on the two different, yet simultaneous, operationalizations of CAC. This method of analyzing CAC could inform future analyses of CAC and inform subsequent discussions about the nature of its potential to inform long-term CVD risk and heart events.     

Exploring the variability of DNA molecules via principal geodesic analysis on the shape space

Most of the linear statistics deal with data lying in a Euclidean space. However, there are many examples, such as DNA molecule topological structures, in which the initial or the transformed data lie in a non-Euclidean space. To get a measure of variability in these situations, the principal component analysis (PCA) is usually performed on a Euclidean tangent space as it cannot be directly implemented on a non-Euclidean space. Instead, principal geodesic analysis (PGA) is a new tool that provides a measure of variability for nonlinear statistics. In this paper, the performance of this new tool is compared with that of the PCA using a real data set representing a DNA molecular structure. It is shown that due to the nonlinearity of space, the PGA explains more variability of the data than the PCA.     

On the robustness properties for maximum likelihood estimators of parameters in exponential power and generalized T distributions*

Abstract

Examining the robustness properties of maximum likelihood (ML) estimators of parameters in exponential power and generalized t distributions has been considered together. The well-known asymptotic properties of ML estimators of location, scale and added skewness parameters in these distributions are studied. The ML estimators for location, scale and scale variant (skewness) parameters are represented as an iterative reweighting algorithm (IRA) to compute the estimates of these parameters simultaneously. The artificial data are generated to examine performance of IRA for ML estimators of parameters simultaneously. We make a comparison between these two distributions to test the fitting performance on real data sets. The goodness of fit test and information criteria approve that robustness and fitting performance should be considered together as a key for modeling issue to have the best information from real data sets.     

A cautionary note on generalized linear models for covariance of unbalanced longitudinal data

Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations.     

Attitudes and Beliefs About Hepatitis B Vaccination Among Gay Men: The Birmingham Measurement Study

ABSTRACT

This study was designed to develop reliable and valid quantitative measures of the beliefs and attitudes toward hepatitis B virus (HBV) vaccination among gay men, using a community-based participatory research approach. A convenience sample of 358 gay men was recruited in two gay bars. Exploratory factor analysis performed on the first split-half sample was used to assess the factor structure from five scales measuring beliefs about HBV vaccination. The scales were based on constructs from the health belief model and the social cognitive theory. Confirmatory factor analysis was performed on the remaining half-sample using structural equation modeling. Multivariable analysis further validated the scales. These findings propose standardized measures that may be useful in assessing the beliefs and attitudes of gay men toward HBV vaccination to guide intervention design and evaluation.     

关键基础设施及其安全管理

关键基础设施(CI)系统是复杂的、高度相互依存的、网络化的社会-技术系统,一旦系统被中断或者被破坏,将会对国民健康、国土安全、经济稳定以及政府的正常运转等产生重大的影响.主要研究关键基础设施及其安全管理过程中的若干问题,对CI的特点进行了分析和总结,建立了网络化CI系统的体系结构,并利用视图模型来描述系统,同时还给出了灾难处理的一般过程.最后,进一步探讨了如何使用系统工程的方法来分析和模拟网络化CI系统的运行方式、潜在的薄弱环节以及改进这些薄弱环节的实施方法.     

数学与数学建模

本文阐述了数学建模与数学应用、数学教学改革等方面的关系 ,指出数学建模对数学教育的重要作用与意义。