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We introduce a class of partial correlation network models with a community structure for large panels of time series. In the model, the series are partitioned into latent groups such that correlation is higher within groups than between them. We then propose an algorithm that allows one to detect the communities using the eigenvectors of the sample covariance matrix. We study the properties of the procedure and establish its consistency. The methodology is used to study real activity clustering in the United States. [ABSTRACT FROM AUTHOR]

Epidemiology is concerned with determining the distribution and causes of disease. Throughout its history, epidemiology has drawn upon statistical ideas and methods to achieve its aims. Because of the exponential growth in our capacity to measure and analyze data on the underlying processes that define each person's state of health, there is an emerging opportunity for population-based epidemiologic studies to influence health decisions made by individuals in ways that take into account the individuals' characteristics, circumstances, and preferences. We refer to this endeavor as "individualized health." The present article comprises 2 sections. In the first, we describe how graphical, longitudinal, and hierarchical models can inform the project of individualized health. We propose a simple graphical model for informing individual health decisions using population-based data. In the second, we review selected topics in causal inference that we believe to be particularly useful for individualized health. Epidemiology and biostatistics were 2 of the 4 founding departments in the world's first graduate school of public health at Johns Hopkins University, the centennial of which we honor. This survey of a small part of the literature is intended to demonstrate that the 2 fields remain just as inextricably linked today as they were 100 years ago. [ABSTRACT FROM AUTHOR]

Variants of path models have been widely used for the analysis of the social status attainment process. The methods presented here differ from earlier approaches in several ways. Social status is considered a categorical variable and path models are developed starting from graphical models, using the marginal log-linear approach. Overall model fit may be tested by standard techniques. Under these models, the status attainment process is completely characterized by a set of parameters that measure the strengths of the relevant effects. This is in sharp contrast with estimating and interpreting ad hoc parameters, without paying attention to overall model fit and to other effects influencing the process. The method is applied to the social status attainment process in the USA, Hungary and Czechoslovakia at the end of the last century, and shows that policies in the latter socialist countries to prevent status inheritance had little success. [ABSTRACT FROM AUTHOR]