Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university education on the internet, free of charge, a real possibility. At this pivotal moment it is appropriate to explore the potential for obtaining comprehensive bioinformatics training with currently existing free video resources. This article pre-sents such a bioinformatics curric-ulum in the form of a virtual course catalog, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in this field. Online Learning Comes of Age Online academic ‘‘courseware’ ’ at the university level has now been available to the public for a decade, the earliest concerted effort having originated in 2002 with the Massachusetts Institute of Technology (MIT) and their OpenCour-seWare initiative

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Parametric max-stable processes are increasingly used to model spatial extremes. Since the dependence structure is specified for block maxima, the data used for inference are block maxima from all sites. To improve the estimation efficiency, we propose a two-step approach with composite likelihood that utilizes site-wise daily records in addition to block maxima. Besides the parameter estimation, there is no formal model checking and diagnosis method for spatial extremes modeling yet. Model diagnosis in practice has been informal and mostly based on visual checking tools such as residual plot and quantile-quantile plot. We proposed a goodness-of-fit test for max-stable processes based on the comparison between a nonparametric and a parametric estimator of the corresponding unknown multivariate Pickands dependence function. The proposed two-step procedure separates the estimation of marginal parameters and dependence parameters into two steps. The first step estimates the marginal pa-rameters with an independence Likelihood by ignoring the spatial dependence. Given ithe marginal parameter estimates, the second step estimates the dependence parameters