Wenxin Jiang

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Food webs aim to provide a thorough representation of the trophic interactions found in an ecosystem. The complexity of empirical food webs, however, is leading many ecologists to focus dynamic ecosystem studies on smaller microcosm or mesocosm studies based upon community modules, which comprise three to five species and the interactions likely to have(More)
Recent experiments and theoretical studies show that AdaBoost can over t in the limit of large time. If running the algorithm forever is suboptimal, a natural question is how low can the prediction error be during the process of AdaBoost? We show under general regularity conditions that during the process of AdaBoost a consistent prediction is generated,(More)
Statistical methodology is presented for the regression analysis of multiple events in the presence of random eeects and measurement error. Omitted covariates are modeled as random eeects. Our approach to parameter estimation and signiicance testing is to start with a naive model of semi-parametric Poisson process regression, and then to adjust for random(More)
Microorganisms have attracted worldwide attention as possible agents for inhibiting water blooms. Algae are usually indirectly inhibited and degraded by secretion from microorganisms. In this study, algal cultures Microcystis aeruginosa (Ma) FACH-918, Microcystis flos-aquae (Mf) FACH-1028, Oocystis borgei (Ob) FACH-1108, and M. aeruginosa PCC 7806 were(More)
1 2 When studying the training error and the prediction error for boosting, it is often assumed that the hypotheses returned by the base learner are weakly accurate, or are able to beat a random guesser by a certain amount of diierence. It is has been an open question how much this diierence can be, whether it will eventually disappear in the boosting(More)
Statistical methodology is presented for the statistical analysis of nonlinear measurement error models. Our approach is to provide adjustments for the usual maximum likelihood es-timators, their standard errors and associated signiicance tests in order to account for the presence of measurement error in some of the covariates. We illustrate the technique(More)
Recent advances in human neuroimaging have shown that it is possible to accurately decode how the brain perceives information based only on non-invasive functional magnetic resonance imaging measurements of brain activity. Two commonly used statistical approaches, namely, univariate analysis and multivariate pattern analysis often lead to distinct patterns(More)
The notion of a boosting algorithm was originally introduced by Valiant in the context of the “probably approximately correct” (PAC) model of learnability [19]. In this context boosting is a method for provably improving the accuracy of any “weak” classification learning algorithm. The first boosting algorithm was invented by Schapire [16] and the second(More)
We derive some simple relations that demonstrate how the posterior convergence rate is related to two driving factors: a "penalized divergence" of the prior, which measures the ability of the prior distribution to propose a nonnegligible set of working models to approximate the true model and a "norm complexity" of the prior, which measures the complexity(More)
The notion of a boosting algorithm was originally introduced by Valiant in the context of the “probably approximately correct” (PAC) model of learnability [19]. In this context boosting is a method for provably improving the accuracy of any “weak” classification learning algorithm. The first boosting algorithm was invented by Schapire [16] and the second(More)