Maria Giovanna Ranalli

Learn More
We propose a new small area estimation approach that combines small area random effects with a smooth, nonparametrically specified trend. By using penalized splines as the representation for the nonparametric trend, it is possible to express the small area estimation problem as a mixed effect regression model. We show how this model can be fitted using(More)
Nonparametric regression is widely used as a method of characterising a non-linear relationship between a variable of interest and a set of covariates. Practical application of nonparametric regression methods in the field of small area estimation is fairly recent, and has so far focussed on the use of empirical best linear unbiased prediction under a model(More)
A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson(More)
β-Blockers are less effective than other antihypertensive drug classes in reducing central systolic blood pressure (cSBP) as compared with peripheral SBP (pSBP). Whether this effect is less pronounced with vasodilating β-blockers (VBB) when compared with nonvasodilating β-blockers (NVBB) remains unsettled. We conducted a systematic review and meta-analysis(More)
This paper aims at classifying, on the basis of their disability profile, the population of elderly and quantifying the number of those with a very low level of functioning in a central region of Italy. This is accomplished using a set of variables on the difficulty of accomplishing everyday tasks (Activities of Daily Living, ADL) and functions. This issue(More)
Data from complex survey designs require special consideration with regard to estimation of finite population parameters and corresponding variance estimation procedures, as a consequence of significant departures from the simple random sampling assumption. In the past decade a number of statistical software packages have been developed to facilitate the(More)
Quantile regression investigates the conditional quantile functions of a response variables in terms of a set of covariates. Mquantile regression extends this idea by a “quantile-like” generalization of regression based on influence functions. In this work we extend it to nonparametric regression, in the sense that the M-quantile regression functions do not(More)