A stickiness coefficient for longitudinal data

In this paper, we introduce the stickiness coefficient, a summary statistic for time-course and longitudinal data, which is designed to characterize the time dynamics of such data. The stickiness coefficient provides a simple, intuitive and informative measure that captures key information contained in time-course data. Under the assumption that the data are generated by the trajectories of a smooth underlying stochastic process, the stickiness coefficient illuminates the relationship between the value of the process at one time with the value it assumes at another time via a single numeric measure. In particular, the stickiness coefficient summarizes the extent to which deviations from the mean trajectory tend to co-vary over time. The estimation scheme we propose will allow for estimation even in the case that the longitudinal data are sparsely observed at irregular times and may be corrupted by noise. We demonstrate an estimation procedure for the stickiness coefficient and establish asymptotic consistency as well as asymptotic convergence rates. We illustrate the resulting stickiness coefficient with some theoretical calculations as well as several economic and health related data examples.