1,231 research outputs found

    Snow cover monitoring by machine processing of multitemporal LANDSAT MSS data

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    LANDSAT frames were geometrically corrected and data sets from six different dates were overlaid to produce a 24 channel (six dates and four wavelength bands) data tape. Changes in the extent of the snowpack could be accurately and easily determined using a change detection technique on data which had previously been classified by the LARSYS software system. A second phase of the analysis involved determination of the relationship between spatial resolution or data sampling frequency and accuracy of measuring the area of the snowpack

    The use of mixtures for dealing with non-normal regression errors

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    In many situations, the distribution of the error terms of a linear regression model departs significantly from normality. It is shown, through a simulation study, that an effective strategy to deal with these situations is fitting a regression model based on the assumption that the error terms follow a mixture of normal distributions. The main advantage, with respect to the usual approach based on the least-squares method is a greater precision of the parameter estimates and confidence intervals. For the parameter estimation we make use of the EM algorithm, while confidence intervals are constructed through a bootstrap method

    Testing for positive association in contingency tables with fixed margins

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    An exact conditional approach is developed to test for certain forms of positive association between two ordinal variables (e.g. positive quadrant dependence, total positivity of order 2). The approach is based on the use of a test statistic measuring the goodness-of-(t of the model formulated according to the type of positive association of interest. The nuisance parameters, corresponding to the marginal distributions of the two variables, are eliminated by conditioning the inference on the observed margins. This, in turn, allows to remove the uncertainty on the conclusion of the test, which typically arises in the unconditional context where the null distribution of the test statistic depends on such parameters. Since the multivariate generalized hypergeometric distribution, which results from conditioning, is normally intractable, Markov chain Monte Carlo methods are used to obtain maximum likelihood estimates of the parameters of the constrained model. The Pearson\u2019s chi-squared statistics is used as a test statistic; a p-value forthis statistic is computed through simulation, when the data are sparse, or exploiting the asymptotic theory based on the chi-bar squared distribution. The extension of the present approach to deal with bivariate contingency tables, strati(ed according to one or more explanatory discrete variables, is also outlined. Finally, three applications based on real data are presented

    Bayesian inference for marginal models under equality and inequality constraints

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    We develop a Bayesian framework for making inference on a class of marginal models for categorical variables, which is formulated through equality and/or inequality constraints on generalized logits, generalized log-odds ratios and similar higher-order interactions. A Markov chain Monte Carlo (MCMC) algorithm is used for parameters estimation and for computing the Bayes factor between competing models. The approach is illustrated through the application to a well-known dataset on social mobility

    An improved geometric inequality via vanishing moments, with applications to singular Liouville equations

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    We consider a class of singular Liouville equations on compact surfaces motivated by the study of Electroweak and Self-Dual Chern-Simons theories, the Gaussian curvature prescription with conical singularities and Onsager's description of turbulence. We analyse the problem of existence variationally, and show how the angular distribution of the conformal volume near the singularities may lead to improvements in the Moser-Trudinger inequality, and in turn to lower bounds on the Euler-Lagrange functional. We then discuss existence and non-existence results.Comment: some references adde

    A nonparametric multidimensional latent class IRT model in a Bayesian framework

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    We propose a nonparametric Item Response Theory model for dichotomously scored items in a Bayesian framework. Partitions of the items are defined on the basis of inequality constraints among the latent class success probabilities. A Reversible Jump type algorithm is described for sampling from the posterior distribution. A consequence is the possibility to make inference on the number of dimensions (i.e., number of groups of items measuring the same latent trait) and to cluster items when unidimensionality is violated

    A historical perspective on vascular plants endemic to Italy

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    A Historical Perspective on Vascular Plants Endemic to Italy. According to a recent review, Italian endemic vascular flora is made up by 1371 specific and subspecific taxa. Focussing on these taxa, in this paper we analyse the frequency of the names' authorities, the type and frequency of specific/infraspecific epithets, and their change over time. The most represented authorities, accounting for about 20% of the name descriptions, are Salvatore Brullo (1947-), Giovanni Gussone (1787-1866) and Michele Tenore (1780-1861). Geographical epithets are the most represented in the dataset. Despite a very slow increase in taxa description in the period 1929-1964, in the last decades we encountered an exponential increase, highlighting for the generalized use of new techniques as a tool to describe new species and for the increasing exploration of poorly known areas, but also for the urgent need to reconsider the past, present and future concept od species

    fcpowered rbs data analysis and system optimization

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    Abstract The previous works on the use of PEM Fuel Cell based power supply system for the operation of off-grid RBS (Radio Base Stations) sites showed a strong influence of system design parameters on the energy conversion performance. In this paper a perturbation of system design is performed through validated models to understand better the variability of performance over a full year operation. Results show that a ratio of energy produced by fossil over energy produced by renewables sources of 0.2 can be reached slightly increasing the photovoltaic plant size without affecting drastically the renewable exploitation. Moreover a positive Net Present Value can be achieved in comparison with the traditional diesel genset solution (from 260k€ to 350k€). The NPV value increases with the PV size and with a reduction of the battery size that leads to a steep reduction in the RES exploitation. Therefore, an optimum has to be sought as a compromise between the two aspects
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