China's massive transformation in urban governance, which is characterized by breaking the link between workplace and residence and consolidating state territorial agencies at the grassroots level, has profound implications for urban residents. While community building is employed as a...

Employing a variation of the Small World technique for tracing social relations in the context of a larger social structure, the instrumental uses of social relations are examined in terms of the prestige and types of relations characteristic of participants m the search process. The results...

Functional linear regression has been widely used to model the relationship between a scalar response and functional predictors. If the original data do not satisfy the linear assumption, an intuitive solution is to perform some transformation such that transformed data will be linearly related....

A stochastic search variable selection approach is proposed for Bayesian model selection in binary and tobit quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the asymmetric Laplace...

The main goal of this research is to better understand age differences in the effects of social networks on mental health. Using a social network approach to revise the convoy of social support model (Kahn and Antonucci, 1980), we examine specifically how two aspects of social support networks...

The stress-distress model is examined in the context of a Chinese urban center, Tianjin. Conceptually, the relationship between stress (as reflected in life events) and distress (as reflected in depressed symptoms) is hypothesized to hold across societies. At the same time, the measures of...

Many statistical models, e.g. regression models, can be viewed as conditional moment restrictions when distributional assumptions on the error term are not assumed. For such models, several estimators that achieve the semiparametric efficiency bound have been proposed. However, in many studies,...