This paper develops nonparametric methods for the survival analysis of epidemic data based on contact intervals. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. We show that the Nelson-Aalen estimator produces an u...

We use a semisupervised learning algorithm based on a topological data analysis approach to assign functional categories to yeast proteins using similarity graphs. This new approach to analyzing biological networks yields results that are as good as or better than state of the art existing approaches.

Multivariate longitudinal data are common in medical, industrial and social science research. However, statistical analysis of such data in the current literature is restricted to linear or parametric modeling, which is inappropriate for applications in which the assumed parametric models are invalid. On the other hand, all existing nonparametric methods for analyzing longitudinal data are for ...

One of the principal goals of the quasar investigations is to study luminosity evolution. A convenient one-parameter model for luminosity says that the expected log luminosity, T ∗, increases linearly as θ0 · log(1+ Z∗), and T ∗(θ0) = T ∗ − θ0 · log(1 + Z∗) is independent of Z∗, where Z∗ is the redshift of a quasar and θ0 is the true value of evolution parameter. Due to experimental constraints...

Group testing is a procedure employed to reduce the cost and increase the speed of large screening studies where infection or contamination of individuals is detected by a test carried out on a sample of, for example, blood, urine, water, etc. Instead of testing the sample of each individual, the method consists in pooling samples of groups of several individuals, and test those pooled samples....

Nonparametric approaches have recently emerged as a flexible way to model longitudinal data. This entry reviews some of the common nonparametric approaches to incorporate time and other covariate effects for longitudinally observed response data. Smoothing procedures are invoked to estimate the associated nonparametric functions, but the choice of smoothers can vary and is often subjective. Bot...

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