Univariate and multivariate models of positive and negative networks: Liking,disliking, and bully–victim relationships

Three relations between elementary school children were investigated: networks of general dislike and bullying were related to networks of general like. These were modeled using multivariate cross-sectional (statistical) network models. Exponential random graph models for a sample of 18 classrooms, numbering 393 students, were summarized using meta-analyses. Results showed (balanced) network structures with positive ties between those who were structurally equivalent in the negative network. Moreover, essential structural parameters for the univariate network structure of positive (general like) and negative (general dislike and bullying) tie networks were identified. Different structures emerged in positive and negative networks. The results provide a starting point for further theoretical and (multiplex) empirical research about negative ties and their interplay with positive ties.     

Apron strings of working mothers: Maternal employment and housework in cross-national perspective

This paper asks whether maternal employment has a lasting influence on the division of household labor for married women and men. Employing multi-level models with 2002 ISSP survey data for 31 countries, we test the lagged accommodation hypothesis that a long societal history of maternal employment contributes to more egalitarian household arrangements. Our results find that living in a country with a legacy of high maternal employment is positively associated with housework task-sharing, even controlling for the personal socialization experience of growing up with a mother who worked for pay. In formerly socialist countries, however, there is less gender parity in housework than predicted by the high historical level of maternal employment.     

Evolution of product quality in European,Japanese and US automotive firms: An exploratory longitudinal analysis

For years we have been hearing that US automobile manufacturers have been losing market share to their Japanese rivals who are reputed to make better quality vehicles. Most such reports are based on the initial quality surveys on new automobiles. In this paper we address two exploratory questions: (1) how does the quality of an automobile change with its age, and, (2) can firm level variables help explain differences quality. To answer these questions, we collected Consumer Reports’ reliability ratings on approximately 300 automobile models made by European, Japanese and US automotive firms during the 1998–2007; and approximately 240 models made by these firms over period of 2008–2015. For both periods we found that not only do automobiles made by Japanese firms have higher initial quality, but, as automobiles get older the difference in the product quality between Japanese versus European and US firms increases. We also found that the more generalist a European or US automobile firm, i.e., the wider is the firm׳s product offering in the marketplace, the lower its overall automobile quality during the 1998–2007 period. Conversely, Japanese generalist firms were found to have higher quality than specialist firms over the same period. The result is partly explained by the fact that Japanese firms have taken a different path to broadening their product variety – they have ensured a high level of quality of their initial offerings before entering newer market segments. The rate of reliability decline was found to be slower for all firms, and the differences in reliability across the 3 groups of firms were much less pronounced during the 2008–2015 period. This improvement may be as result of restructuring done by US automobile firms.     

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.     

潜在成长模型在体育运动领域的应用

介绍了潜在成长模型的发展、类型和内容,通过分析、归纳、整理等,比较了潜在成长模型与传统t检验、方差分析、回归分析的差别,探讨了潜在成长模型研究设计的要求,通过台湾棒球职业球员薪酬成长模型说明。利用文献调查法,分析了LGM潜在成长模型在体育运动领域中应用的概况,举例说明体育运动领域应用LGM的情况,证实了未来潜在成长模型会在体育科学领域逐渐受到重视并得到快速发展。     

The time‐dependent “cure‐death” model investigating two equally important endpoints simultaneously in trials treating high‐risk patients with resistant pathogens

A variety of primary endpoints are used in clinical trials treating patients with severe infectious diseases, and existing guidelines do not provide a consistent recommendation. We propose to study simultaneously two primary endpoints, cure and death, in a comprehensive multistate cure‐death model as starting point for a treatment comparison. This technique enables us to study the temporal dynamic of the patient‐relevant probability to be cured and alive. We describe and compare traditional and innovative methods suitable for a treatment comparison based on this model. Traditional analyses using risk differences focus on one prespecified timepoint only. A restricted logrank‐based test of treatment effect is sensitive to ordered categories of responses and integrates information on duration of response. The pseudo‐value regression provides a direct regression model for examination of treatment effect via difference in transition probabilities. Applied to a topical real data example and simulation scenarios, we demonstrate advantages and limitations and provide an insight into how these methods can handle different kinds of treatment imbalances. The cure‐death model provides a suitable framework to gain a better understanding of how a new treatment influences the time‐dynamic cure and death process. This might help the future planning of randomised clinical trials, sample size calculations, and data analyses.     

Interquantile shrinkage in additive models

In this paper, we investigate the commonality of nonparametric component functions among different quantile levels in additive regression models. We propose two fused adaptive group Least Absolute Shrinkage and Selection Operator penalties to shrink the difference of functions between neighbouring quantile levels. The proposed methodology is able to simultaneously estimate the nonparametric functions and identify the quantile regions where functions are unvarying, and thus is expected to perform better than standard additive quantile regression when there exists a region of quantile levels on which the functions are unvarying. Under some regularity conditions, the proposed penalised estimators can theoretically achieve the optimal rate of convergence and identify the true varying/unvarying regions consistently. Simulation studies and a real data application show that the proposed methods yield good numerical results.     

Estimation and inference of the joint conditional distribution for multivariate longitudinal data using nonparametric copulas

In this paper we study estimating the joint conditional distributions of multivariate longitudinal outcomes using regression models and copulas. For the estimation of marginal models, we consider a class of time-varying transformation models and combine the two marginal models using nonparametric empirical copulas. Our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Empirical copulas combined with time-varying transformation models may allow quite flexible modelling for the joint conditional distributions for multivariate longitudinal data. We derive the asymptotic properties for the copula-based estimators of the joint conditional distribution functions. For illustration we apply our estimation method to an epidemiological study of childhood growth and blood pressure.     

Testing independence based on Bernstein empirical copula and copula density

In this paper we provide three nonparametric tests of independence between continuous random variables based on the Bernstein copula distribution function and the Bernstein copula density function. The first test is constructed based on a Cramér-von Mises divergence-type functional based on the empirical Bernstein copula process. The two other tests are based on the Bernstein copula density and use Cramér-von Mises and Kullback–Leibler divergence-type functionals, respectively. Furthermore, we study the asymptotic null distribution of each of these test statistics. Finally, we consider a Monte Carlo experiment to investigate the performance of our tests. In particular we examine their size and power which we compare with those of the classical nonparametric tests that are based on the empirical distribution function.