1,721,009 research outputs found
Credito al consumo e fragilità finanziaria
No full textThe paper focuses on the consumer credit market in Italy and on the related risk of over-indebtedness. Using survey data, it investigates the impact of over-indebtedness on consumption behaviour, evaluating in particular if consumer credit is used to cover gaps in income and if this is associated with an increased and diffused inadequacy of financial and economic position of indebted households. Results highlight that a relatively consistent part of consumer credit is concentrated in the hands of financially fragile individuals. Moreover, when considering the amount of debt measured in relative terms, a difficult financial position adds 2,7 percentage points to the risk of over-indebtedness compared to households without any financial difficult
ABS06 - 2006 Applied Bayesian Statistics School HIERARCHICAL MODELLING APPROACHES FOR SPATIAL DATA IN ENVIRONMENTAL AND HEALTH SCIENCES Bertinoro (FC), Italy 17 - 21 July, 2006
No full textCNR-IMATI (Istituto di Matematica Applicata e Tecnologie Informatiche at Consiglio Nazionale delle Ricerche) and the University of Pavia (DEPMQ), in cooperation with BAYSTAT, are planning to organise every year a School on state-of-the-art Bayesian applications, inviting leading experts in the field.
ABS06 will be organised in cooperation with the Department of Statistics of the University of Bologna and the GRASPA group and it will be held in the University Residential Centre of Bertinoro.
The topic chosen for the 2006 school is Hierarchical Modelling Approaches for Spatial Data in Environmental and Health Sciences.
The lecturer will be Sudipto Banerjee (University of Minnesota, USA), assisted by Fedele Greco (University of Bologna, Italy) and Luca La Rocca (University of Modena and Reggio, Italy).
The school will explore the use of Bayesian approaches for spatial data in environmental and health sciences.
The school will make use of lectures, practical sessions, software demonstrations, informal discussion sessions and presentations of research projects by school participants
L’influenza del contesto familiare sulla sopravvivenza dei bambini. Risultati di indagini microdemografiche sull’Italia dell’Ottocento
No full textA transitional non-parametric maximum pseudo-likelihood estimator for disease mapping
No full textComputational Statistics & Data Analysi
Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health
No full textSince mid-1990s, Generalised Additive Models (GAM) became very popular for the analysis of short-term effects of air pollution on health. Such approach involves specification of non parametric functions to adjust for confounding effect of unobserved variables with a systematic temporal behaviour and to model weather variables and influenza epidemics. Recently critical points in using commercial statistical software for fitting GAMs were stressed (Dominici et al., 2002; Ramsey et al., 2003) and some reanalyses of time series data on air pollution and health were performed. This new attention to semi-parametric models has led researchers to consider alternative estimation methods for GAMs and to wonder whether simpler parametric models can be a better choice than GAMs (Lumley and Sheppard, 2003). The purpose of this work is to show by simulation analyses some of the problems which we could find using GAMs, and to discuss real advantages of semi-parametric approach with respect to a fully parametric alternative, based on specification of Generalized Linear Models with natural cubic splines (GLM + NS). Here we considered the situation in which only the smooth function for time trend is included in the model. Generalized Additive Models were fitted by the direct methods implemented in R software (Wood, 2000). Different simulation analyses were performed, varying the "true" number of degrees of freedom for the smooth function, the concurvity amount in data and the "true" size of air pollutant effect. Our simulations show that GAM provide biased estimates of air pollutant effect, the bias being not negligible for moderate concurvity amount and small effect size. We found also that using semi-parametric approach a certain amount of undersmoothing is needed to obtain appropriated estimation of risk. Good performance was obtained selecting the smoothing parameter by Generalized Cross Validation. On the contrary analysis of partial autocorrelation of residuals from GAM brings to inappropriate model selection. GLM+NS is a good alternative to semi-parametric approach, resulting robust to misspecification of degrees of freedom for the spline. However the applicability of such approach should be considered carefully in presence of particular local variations of seasonality or in presence of outliers, because results could be sensitive to knots placement. Moreover the choice of knots positions could be a very important problem in smoothing other covariates like temperature
L'influenza del contesto familiare sulla sopravvivenza dei bambini. Risultati di indagini microdemografiche sull'Italia dell'Ottocento
No full textL'influenza del contesto familiare sulla sopravvivenza dei bambini. Risultati di indagini microdemografiche sull'Italia dell'Ottocento
No full textA hierarchical Bayesian model for space-time variation of disease risk
No full text In this paper we propose a hierarchical Bayesian model to study the variation in space and time of disease risk. We represent spatial effects following the usual Bayesian specification of a Gaussian convolution of unstructured and structured components, while we adopt the birth cohort (instead of the commonly used period of death) as the main time scale. The model also includes space-time interaction terms to take into account structured inseparable space-time variability. The model is applied to lung cancer death certificate data in Tuscany, for males during the period 1971-94. While a calendar period analysis points out a general increase of mortality levelling off in the last period (1990-94), the cohort model shows a general and substantial decrease of the relative risk for the youngest cohorts born after 1930. Moreover, the pattern of the epidemic by birth cohort presents a maximum which varies by municipalities, with a strong north-west/south-east gradient. </jats:p
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