This work investigates the effect of ecological interactions between organisms on the evolutionary dynamics of a community. A spatially explicit, individual based model is presented, in which organisms compete for space and for resources. We investigated how introducing the potential for mutualistic relationships (where the presence of one type of organism stimulates the growth of another type, and vice versa) affected the evolutionary dynamics of the system. Without this potential, one or a small number of individual types of organisms dominated the simulated community from the onset. When mutualistic relationships were allowed, many persisting types arose, with new types appearing continually. Furthermore, we investigated how the stability of the community differed when mutualistic relationships were allowed and disallowed. Our results suggest that the existence of mutualistic relationships improved community stability. KEYWORDS: Ecosystem; evolution; ecological interaction; mutualism; ecosystem diversity; ecosystem stability 1.

We consider the problem of estimating occurrence rates of rare events for extremely sparse data, using pre-existing hierarchies to perform inference at multiple resolutions. In particular, we focus on the problem of estimating click rates for (webpage, advertisement) pairs (called impressions) where both the pages and the ads are classified into hierarchies that capture broad contextual information at different levels of granularity. Typically the click rates are low and the coverage of the hierarchies is sparse. To overcome these difficulties we devise a sampling method whereby we analyze a specially chosen sample of pages in the training set, and then estimate click rates using a two-stage model. The first stage imputes the number of (webpage, ad) pairs at all resolutions of the hierarchy to adjust for the sampling bias. The second stage estimates click rates at all resolutions after incorporating correlations among sibling nodes through a tree-structured Markov model. Both models are scalable and suited to large scale data mining applications. On a real-world dataset consisting of 1/2 billion impressions, we demonstrate that even with 95 % negative (non-clicked) events in the training set, our method can effectively discriminate extremely rare events in terms of their click propensity.

In this paper we consider time series models belonging to the AR(autoregressive) familiy and deal with the estimation of the residual variance. This is important because estimates of the variance enter, for example, into confidence sets for the parameters of the model, in the estimation of the spectrum , in expressions for the estimated error of prediction and in sample quantities used to make inferences about the order of the model. We consider the asymptotic biases for moment and least squares estimators of the residual variance, and compare them with known results when available and with those for maximum likelihood estimators under normality. For finite samples, simulation results are presented. Key words: AR models, bias, least squares estimator, maximum likelihood estimator, moment estimator, residual variance, time series. 1. Introduction. We consider time series models belonging to the AR(p) family in which the observable stationary process fX t g has EfX t g = ¯ and finite...

Abstract: Rapid urbanization and its implications for both water quality issues and floods have increased the need for modeling of urban drainage systems. Many operational models are based on deterministic solutions of hydraulic equations. Improving such models by adding a “black-box ” component to deal with any systematic structure in the residuals is proposed. In this study, a conventional deterministic stormwater drainage network model is first developed for a rapidly developing catchment using the HYDROWORKS �now called Infoworks � package, from Wallingford Software in the United Kingdom. However, despite the generally satisfactory results, the HYDROWORKS model tended to underestimate the flow volume. In this paper, a black-box or “systems ” model is fitted to the hydraulic urban drainage model in order to improve its overall efficiency. A study was conducted of suitable black-box models, which included the nonlinear artificial neural network model �ANN�, and the linear time series models of Box and Jenkins in 1976. They were added to either the output �in simulation mode � or, in updating mode, to the residuals �i.e., difference between modeled and measured output � of the deterministic hydraulic model. The updating procedure provided a considerable improvement in the overall model efficiency for different lead-time forecasting. In simulation mode, however, only the nonlinear ANN model gave better performance in calibration, and a slight improvement in validation.

this paper is to study how bootstrap selection criteria perform. These criteria are based on a weighted mean of the apparent errors in the sample, and the average error rate obtained from bootstrap samples not containing the point being predicted. We also want to compare these new measures with the traditional ones based on AIC

The article deals with the possible methodology of processing of data and information for the search of prediction of heat supply daily diagram (HSDD). The methodology includes technical analyses-regression analysis, dynamic models, fuzzy logic, artificial neural networks, genetic algorithms, chaos analysis, hybrid models, dynamical simulations and other methods. The process of decision making and evaluation are also mentioned.

Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in covariance estimation, particularly in the recent high-dimensional data environment where enforcing the positive-definiteness constraint could be computationally expensive. We provide a survey of the progress made in modeling covariance matrices from the perspectives of generalized linear models (GLM) or parsimony and use of covariates in low dimensions, regularization (shrinkage, sparsity) for high-dimensional data, and the role of various matrix factorizations. A viable and emerging regressionbased setup which is suitable for both the GLM and the regularization approaches is to link a covariance matrix, its inverse or their factors to certain regression models and then solve the relevant (penalized) least squares problems. We point out several instances of this regression-based setup in the literature. A notable case is in the Gaussian graphical models where linear regressions with LASSO penalty are used to estimate the neighborhood of one node at a time (Meinshausen and Bühlmann, 2006). Some advantages