Search

Abstract This paper discusses regression analysis of incomplete event history studies with a focus on simultaneous estimation and variable selection. Such studies are commonly performed in areas such as medical studies and social sciences, and a great deal of literature has been devoted to their analysis except for the problem considered here (Sun and Zhao, 2013). We develop a new method, which will be referred to as a broken adaptive ridge regression approach. We establish its asymptotic properties, including the oracle property and clustering effect. We also report simulation results which indicate that the proposed method performs well, and better than the existing methods, in practice. In addition, an application is provided. [ABSTRACT FROM AUTHOR]

This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in one’s social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research. [ABSTRACT FROM AUTHOR]