A flexible semiparametric transformation model for survival data

I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested.     

Communicative Correlates of Satisfaction,Family Identity,and Group Salience in Multiracial/Ethnic Families

Guided by the Common Ingroup Identity Model ( S. L. Gaertner & J. F. Dovidio, 2000 ) and Communication Accommodation Theory ( C. Shepard, H. Giles, & B. A. LePoire, 2001 ), we examined the role of identity accommodation, supportive communication, and self‐disclosure in predicting relational satisfaction, shared family identity, and group salience in multiracial/ethnic families. Additionally, we analyzed the association between group salience and relational outcomes as well as the moderating roles of multiracial/ethnic identity and marital status. Individuals who have parents from different racial/ethnic groups were invited to complete questionnaires on their family experiences. Participants (N = 139) answered questions about relationships with mothers, fathers, and grandparents. The results of the multilevel modeling analyses are discussed in terms of implications for understanding multiracial/ethnic families and family functioning.     

The exponentiated reduced Kies distribution: Properties and applications

Here we consider an exponentiated version of the reduced Kies distribution and discuss some of its properties. The parameters of the distribution are estimated by the method of maximum likelihood and illustrated with the help of certain real-life data sets. Asymptotic behavior of the maximum likelihood estimators of the parameters of the distribution is also studied by using certain simulated data sets.     

The effect of different data structures,sample sizes on model fit measures

In this study, our aim was to investigate the changes of different data structures and different sample sizes on the structural equation modeling and the influence of these factors on the model fit measures. Examining the created structural equation modeling under different data structures and sample sizes, the evaluation of model fit measures were performed with a simulation study. As a result of the simulation study, optimization and negative variance estimation problems have been encountered depending on the sample size and changing correlations. It was observed that these problems disappeared either by increasing the sample size or the correlations between the variables in factor. For upcoming studies, the choice of RMSEA and IFI model fit measures can be suggested in all sample sizes and the correlation values for data sets are ensured the multivariate normal distribution assumption.     

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio‐temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio‐temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio‐temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin‐wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.     

Effects of key enabling technologies for seru production on sustainable performance

Demand uncertainty, economic globalization, and environmental deterioration force factories to innovate their manufacturing systems for achieving sustainable performance. Seru production, which is the latest manufacturing mode developed in Japan, attracts broad attention from both academia and practitioners. The overwhelming majority of existing works on seru production focus on economic performance unilaterally, while neglecting the environmental and social performance. This paper investigates the effects of key enabling technologies for seru production on sustainable performance. Firstly, four key enabling technologies for seru production are identified through systematic review, and an evaluation indicator system of sustainable performance in the context of seru production is developed. Then, the hypotheses about the effects of the identified key enabling technologies for seru production on sustainable performance are proposed on the basis of previous research achievements, theoretical analysis, and practical observations. Finally, the hypotheses are tested through structural equation modeling. Except for two hypotheses which are not supported and one which is indirectly supported, all other hypotheses are verified. The research results show that the four key enabling technologies for seru production have different effects on the three dimensions of sustainable performance. The achievements of this work are of significance to improve the comprehensive understanding of seru production, as well as to develop practical methods to implement sustainable operations.     

Opinions toward suicide: Cross-national evaluation of cultural and religious effects on individuals

This study evaluates the role that religious and cultural values have on individual opinions toward suicide worldwide. Using multilevel modeling with data from the fourth wave of the World Values Survey (42,299 individuals in 43 countries), the current study is designed to analyze the effect of individual (i.e., micro-level), and country (i.e., macro-level) characteristics on opinions toward suicide. Specifically, cultural values, religious affiliation, religious importance, and church attendance are analyzed at both the individual and country levels to evaluate the impact of individual and country level effects on opinions toward suicide. The results show that individual opinions toward suicide are influenced by individual belief as well as by the cultural and religious characteristics of their country. The results suggest that evaluation of individual opinions toward controversial behavior should account for the unique and cross-interaction effects of micro- and macro-level effects.     

基于过程划分的可再生能源技术扩散模型

把握技术扩散的机理和主要特征是可再生能源发展规划和管理优化的基础。本文基于信息扩散、技术经济、社会认可度等因素对可再生能源技术的影响及其作用机理分析,根据投资者状态变化,将技术扩散过程划分为技术获悉、效益计算和技术认可三个阶段,构建了可再生能源技术扩散(即潜在投资者转变为最终投资者)过程的数理模型。探讨了可再生能源技术扩散的速度有限性、政策效率递减性以及区域差异性三个关键特征,结合算例验证模型并进一步分析了技术扩散的不同驱动力,从而为可再生能源技术扩散机理政策制定提供理论支持和意见建议。     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Effect of aggregation on the estimation of trend in mortality

There are three approaches to analyzing and forecasting age‐specific mortality: (1) analyze age‐specific data directly, (2) analyze each cause‐specific mortality series separately and add the results, (3) analyze cause‐specific mortality series jointly and add the results. We show that if linear models are used for cause‐specific mortality, then the three approaches often give close results even when cause‐specific series are correlated. This result holds for cross‐correlations arising from random misclassification of deaths by cause, and also for certain patterns of systematic misclassification. It need not hold, if one or more causes serve as “leading indicators”; for the remaining causes, or if outside information is incorporated into forecasting either through expert judgment or formal statistical modeling. Under highly nonlinear models or in the presence of modeling error the result may also fail. The results are illustrated with U.S. age‐specific mortality data from 1968–1985. In some cases the aggregate forecasts appear to be the more credible ones.