A Provisional, annotated checklist of the butterflies in lake Manyara National Park, Arusha region, Tanzania

Volume: 8

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.Comment: 12 pages. Statistical Paper

Los Amantes Pasados / Old Flames / 老相好

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Bias correction in a multivariate normal regression model with general parameterization

This paper develops a bias correction scheme for a multivariate normal model under a general parameterization. In the model, the mean vector and the covariance matrix share the same parameters. It includes many important regression models available in the literature as special cases, such as (non)linear regression, errors-in-variables models, and so forth. Moreover, heteroscedastic situations may also be studied within our framework. We derive a general expression for the second-order biases of maximum likelihood estimates of the model parameters and show that it is always possible to obtain the second order bias by means of ordinary weighted lest-squares regressions. We enlighten such general expression with an errors-in-variables model and also conduct some simulations in order to verify the performance of the corrected estimates. The simulation results show that the bias correction scheme yields nearly unbiased estimators. We also present an empirical ilustration.Comment: 1 Figure, 17 page

Software-defined networking: guidelines for experimentation and validation in large-scale real world scenarios

Part 1: IIVC WorkshopInternational audienceThis article thoroughly details large-scale real world experiments using Software-Defined Networking in the testbed setup. More precisely, it provides a description of the foundation technology behind these experiments, which in turn is focused around OpenFlow and on the OFELIA testbed. In this testbed preliminary experiments were performed in order to tune up settings and procedures, analysing the encountered problems and their respective solutions. A methodology consisting of five large-scale experiments is proposed in order to properly validate and improve the evaluation techniques used in OpenFlow scenarios