177,973 research outputs found
Giornalisti in Facoltà/3, 2002-2003
No full textPer il terzo anno consecutivo, la Collana «Studi e Ricerche» del Dipartimento di Scienze storiche, giuridiche, politiche e sociali accoglie un volume intitolato Giornalisti in Facoltà e promosso su iniziativa della Cattedra di Storia del giornalismo della Facoltà di Scienze Politiche. Come di consueto, si tratta della riproduzione pressoché integrale di conferenze tenute da giornalisti e altri professionisti del mondo dell’informazione, nell’ambito di una più ampia serie di Iniziative specifiche affiancate alla regolare attività didattica.
In questo volume sono raccolte le conferenze tenute nell’anno accademico 2002-2003:
MARIO DE GREGORIO Biblioteca pubblica e giornali;
GIANNI TIBERI, Il mestiere di cronista;
DANIELE REDAELLI, 24 ore in una redazione sportiva: modi tempi e luoghi di una giornata-tipo alla Gazzetta dello Sport; con un intervento di RICCARDO PRATESI;
ENRICO ZANCHI, MARCO PALOCCI Solo pubblicisti negli Uffici stampa: un mestiere in costante evoluzione. Il caso del Consiglio regionale toscano e della Camera dei Deputati;
ANTONIO DIPOLLINA Diamo i voti alla TV, con un intervento di RICCARDO PRATESI;
EMANUELE GIORDANA, Le crociate del nuovo millennio e il ruolo dell’informazione;
GIANNI LUCARINI, “Vedere” la radio su Internet, con un intervento di ELISABETTA TANINI
Small area estimation via m-quantile geographically weighted regression
No full textThe effective use of spatial information, that is the geographic locations of population units, in a regression model-based approach to small area estimation is an important practical issue. One approach for incorporating such spatial information in a small area regression model is via Geographically Weighted Regression (GWR). In GWR the relationship between the outcome variable and the covariates is characterised by local rather than global parameters, where local is defined spatially. In this paper we investigate GWR-based small area estimation under the M-quantile modelling approach. In particular, we specify an M-quantile GWR model that is a local model for the M-quantiles of the conditional distribution of the outcome variable given the covariates. This model is then used to define a bias-robust predictor of the small area characteristic of interest that also accounts for spatial association in the data. An important spin-off from applying the M-quantile GWR small area model is that it can potentially offer more efficient synthetic estimation for out of sample areas. We demonstrate the usefulness of this framework through both model-based as well as design-based simulations, with the latter based on a realistic survey data set. The paper concludes with an illustrative application that focuses on estimation of average levels of Acid Neutralizing Capacity for lakes in the north-east of the USA.<br/
ΦΙΛΗ ΠΑΙΣ Figlia Cara, Canto in greco antico e in italiano. -J. Blomquist, R Colace, illustrazioni di F. Pratesi, 2002
No full textCusset Christophe. ΦΙΛΗ ΠΑΙΣ Figlia Cara, Canto in greco antico e in italiano. -J. Blomquist, R Colace, illustrazioni di F. Pratesi, 2002. In: Revue des Études Anciennes. Tome 105, 2003, n°1. p. 349
A single-centre experience on endovascular repair of non-infected extracranial internal carotid artery pseudoaneurysms
No full textIn the period ranging from 2006 to 2010, 5 endovascular interventions for carotid pseudoaneurysm (4 post-carotid endarterectomy [post-CEA] and 1 posttraumatic), without signs of infection, were carried out. All patients were neurologically asymptomatic. A covered stent was used in 4 cases. The fifth patient, undergoing a third endovascular procedure after a re-do open surgical repair of a post-CEA pseudoaneurysm, was treated with a bare stent. The technical success rate was 100%. A type 1 endoleak at the end of the procedure was observed in 1 patient, but it disappeared before discharging. No perioperative neurologic events occurred. At the most recent mean follow-up of 24 months, all patients are alive, without neurologic symptoms, and all have maintained patency of the internal carotid artery and are pseudoaneurysm-free
Small area estimation of the mean using nonparametric M-quantile regression: a comparison when a linear mixed model does not hold
No full textThe demand for reliable statistics in subpopulations, when only reduced sample sizes are available, has promoted the development of small area estimation methods. In particular, an approach that is now widely used is based on the seminal work by Battese et al. [An error-components model for prediction of county crop areas using survey and satellite data, J. Am. Statist. Assoc. 83 (1988), pp. 28–36] that uses linear mixed models (MM). We investigate alternatives when a linear MM does not hold because, on one side, linearity may not be assumed and/or, on the other, normality of the random effects may not be assumed. In particular, Opsomer et al. [Nonparametric small area estimation using penalized spline regression, J. R. Statist. Soc. Ser. B 70 (2008), pp. 265–283] propose an estimator that extends the linear MM approach to the case in which a linear relationship may not be assumed using penalized splines regression. From a very different perspective, Chambers and Tzavidis [M-quantile models for small area estimation, Biometrika 93 (2006), pp. 255–268] have recently proposed an approach for small-area estimation that is based on M-quantile (MQ) regression. This allows for models robust to outliers and to distributional assumptions on the errors and the area effects. However, when the functional form of the relationship between the qth MQ and the covariates is not linear, it can lead to biased estimates of the small area parameters. Pratesi et al. [Semiparametric M-quantile regression for estimating the proportion of acidic lakes in 8-digit HUCs of the Northeastern US, Environmetrics 19(7) (2008), pp. 687–701] apply an extended version of this approach for the estimation of the small area distribution function using a non-parametric specification of the conditional MQ of the response variable given the covariates [M. Pratesi, M.G. Ranalli, and N. Salvati, Nonparametric m-quantile regression using penalized splines, J. Nonparametric Stat. 21 (2009), pp. 287–304]. We will derive the small area estimator of the mean under this model, together with its mean-squared error estimator and compare its performance to the other estimators via simulations on both real and simulated data
Small area estimation of the mean using nonparametric M-quantile regression: a comparison when a linear mixed model does not hold
No full textThe demand for reliable statistics in subpopulations, when only reduced sample sizes are available, has promoted the development of small area estimation methods. In particular, an approach that is now widely used is based on the seminal work by Battese et al. [An error-components model for prediction of county crop areas using survey and satellite data, J. Am. Statist. Assoc. 83 (1988), pp. 28–36] that uses linear mixed models (MM). We investigate alternatives when a linear MM does not hold because, on one side, linearity may not be assumed and/or, on the other, normality of the random effects may not be assumed. In particular, Opsomer et al. [Nonparametric small area estimation using penalized spline regression, J. R. Statist. Soc. Ser. B 70 (2008), pp. 265–283] propose an estimator that extends the linear MM approach to the case in which a linear relationship may not be assumed using penalized splines regression. From a very different perspective, Chambers and Tzavidis [M-quantile models for small area estimation, Biometrika 93 (2006), pp. 255–268] have recently proposed an approach for small-area estimation that is based on M-quantile (MQ) regression. This allows for models robust to outliers and to distributional assumptions on the errors and the area effects. However, when the functional form of the relationship between the qth MQ and the covariates is not linear, it can lead to biased estimates of the small area parameters. Pratesi et al. [Semiparametric M-quantile regression for estimating the proportion of acidic lakes in 8-digit HUCs of the Northeastern US, Environmetrics 19(7) (2008), pp. 687–701] apply an extended version of this approach for the estimation of the small area distribution function using a non-parametric specification of the conditional MQ of the response variable given the covariates [M. Pratesi, M.G. Ranalli, and N. Salvati, Nonparametric m-quantile regression using penalized splines, J. Nonparametric Stat. 21 (2009), pp. 287–304]. We will derive the small area estimator of the mean under this model, together with its mean-squared error estimator and compare its performance to the other estimators via simulations on both real and simulated data
Semiparametric M-quantile regression for estimating the proportion of acidic lakes in 8-digit HUCs of the Northeastern US
No full textThe demand for reliable statistics in subpopulations, when only reduced sample sizes are available, has promoted the development of small area estimation methods. In particular, an approach that is now widely used is based on the seminal work by Battese et al. [An error-components model for prediction of county crop areas using survey and satellite data, J. Am. Statist. Assoc. 83 (1988), pp. 28-36] that uses linear mixed models (MM). We investigate alternatives when a linear MM does not hold because, on one side, linearity may not be assumed and/or, on the other, normality of the random effects may not be assumed. In particular, Opsomer et al. [Nonparametric small area estimation using penalized spline regression, J. R. Statist. Soc. Ser. B 70 (2008), pp. 265-283] propose an estimator that extends the linear MM approach to the case in which a linear relationship may not be assumed using penalized splines regression. From a very different perspective, Chambers and Tzavidis [M-quantile models for small area estimation, Biometrika 93 (2006), pp. 255-268] have recently proposed an approach for small-area estimation that is based on M-quantile (MQ) regression. This allows for models robust to outliers and to distributional assumptions on the errors and the area effects. However, when the functional form of the relationship between the qth MQ and the covariates is not linear, it can lead to biased estimates of the small area parameters. Pratesi et al. [Semiparametric M-quantile regression for estimating the proportion of acidic lakes in 8-digit HUCs of the Northeastern US, Environmetrics 19(7) (2008), pp. 687-701] apply an extended version of this approach for the estimation of the small area distribution function using a non-parametric specification of the conditional MQ of the response variable given the covariates [M. Pratesi, M.G. Ranalli, and N. Salvati, Nonparametric m-quantile regression using penalized splines, J. Nonparametric Stat. 21 (2009), pp. 287-304]. We will derive the small area estimator of the mean under this model, together with its mean-squared error estimator and compare its performance to the other estimators via simulations on both real and simulated data
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