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Valentina Mameli

Valentina Mameli

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31
Publications
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130
Citations

Publications

Publications (31)
Article
In this work we study stationary linear time-series models, and construct and analyse “score-matching” estimators based on the Hyvärinen scoring rule. We consider two scenarios: a single series of increasing length, and an increasing number of independent series of fixed length. In the latter case there are two variants, one based on the full data,...
Article
One of the main problems that the drug discovery research field confronts is to identify small molecules, modulators of protein function, which are likely to be therapeutically useful. Common practices rely on the screening of vast libraries of small molecules (often 1–2 million molecules) in order to identify a molecule, known as a lead molecule,...
Article
Bayesian regression models have been widely studied and adopted in the statistical literature. Many studies consider the development of reliable priors to select the relevant variables and derive accurate posterior predictive distributions. Moreover in the context of small high-dimensional data, where the number of observations is very small with r...
Article
We discuss an approach of robust fitting on nonlinear regression models, both in a frequentist and a Bayesian approach, which can be employed to model and predict the contagion dynamics of COVID‐19 in Italy. The focus is on the analysis of epidemic data using robust dose‐response curves, but the functionality is applicable to arbitrary nonlinear re...
Article
Building clusters for pattern recognition and analysis of geographical areas can be a useful way to provide relevant information for economic and social decisions. In this paper, we introduce a novel spatial clustering technique, called Bootstrap ClustGeo (BCG), which is a hierarchical approach, based on bootstrap techniques with spatial constraint...
Article
Standard Bayesian analyses can be difficult to perform when the full likelihood, and consequently the full posterior distribution, is too complex or even impossible to specify or if robustness with respect to data or to model misspecifications is required. In these situations, we suggest to resort to a posterior distribution for the parameter of in...
Chapter
Currently many research problems are addressed by analysing datasets characterized by a huge number of variables, with a relatively limited number of observations, especially when data are generated by experimentation. Most of the classical statistical procedures for regression analysis are often inadequate to deal with such datasets as they have b...
Preprint
Likelihood-based estimation methods involve the normalising constant of the model distributions, expressed as a function of the parameter. However in many problems this function is not easily available, and then less efficient but more easily computed estimators may be attractive. In this work we study stationary time-series models, and construct a...
Chapter
In this paper we consider frequentist and Bayesian likelihood-based small-sample procedures to compute confidence intervals for Gini’s gamma index in the bivariate Gaussian copula model. We furthermore discuss how the method straightforwardly extends to any measure of concordance which is available in closed form, and to any type of copula for whic...
Article
Scoring rules give rise to methods for statistical inference and are useful tools to achieve robustness or reduce computations. Scoring rule inference is generally performed through first-order approximations to the distribution of the scoring rule estimator or of the ratio-type statistic. In order to improve the accuracy of first-order methods eve...
Article
Most of the methods nowadays employed in forecast problems are based on scoring rules. There is a divergence function associated to each scoring rule, that can be used as a measure of discrepancy between probability distributions. This approach is commonly used in the literature for comparing two competing predictive distributions on the basis of t...
Preprint
Standard Bayesian analyses can be difficult to perform when the full likelihood, and consequently the full posterior distribution, is too complex and difficult to specify or if robustness with respect to data or to model misspecifications is required. In these situations, we suggest to resort to a posterior distribution for the parameter of interes...
Article
Standard Bayesian analyses can be difficult to perform when the full likelihood, and consequently the full posterior distribution, is too complex and difficult to specify or if robustness with respect to data or to model misspecifications is required. In these situations, we suggest to resort to a posterior distribution for the parameter of interes...
Conference Paper
In this paper we present a methodology that can be used to design experiments of complex systems characterized by a huge number of variables. The strategy combines the evolutionary principles with the information provided by statistical models tailored to the problem under consideration. Here, we are concerned with the process of design molecules,...
Article
Full-text available
Several characterizations of Generalized Beta-generated family of distributions, introduced by Alexander et al. (2012), are presented, giving special emphasis to the Kumaraswamy skew-normal distribution and the Beta skew-normal distribution, special cases of this family. These characterizations are based on: (i) a simple relationship between two tr...
Chapter
In this paper we study the Beta skew-normal distribution introduced by Mameli and Musio (2013). Some new properties of this distribution are derived including formulae for moments in particular cases and bi-modality properties. Furthermore, we provide expansions for its distribution and density functions. Bounds for the moments and the variance of...
Article
We consider the use of modern likelihood asymptotics in the construction of confidence intervals for the parameter which determines the skewness of the distribution of the maximum/minimum of an exchangeable bivariate normal random vector. Simulation studies were conducted to investigate the accuracy of the proposed methods and to compare them to av...
Article
We propose a new generalization of the skew-normal distribution (Azzalini, 1985) referred to as the Kumaraswamy skew-normal. The new distribution is computationally more tractable than the Beta skew-normal distribution (Mameli and Musio, 2013) with which it shares some properties.
Article
In this paper we discuss higher-order asymptotic expansions for proper scoring rules generalizing results for likelihood quantities, but meanwhile bring in the difficulty caused by the failure of the information identity. In particular, we derive higher-order approximations to the distribution of the scoring rule estimator, of the scoring rule rati...
Article
Full-text available
The aim of this paper is to compare numerically the performance of two estimators based on Hyv\"arinen's local homogeneous scoring rule with that of the full and the pairwise maximum likelihood estimators. In particular, two different model settings, for which both full and pairwise maximum likelihood estimators can be obtained, have been considere...
Chapter
Full-text available
In this work we propose different spatial models to study hospital recruitment, including some potentially explanatory variables, using data from the hospital of Mulhouse a town located in the north-east of France. Interest is on the distribution over geographical units of the number of patients living in this geographical unit. Models considered a...
Conference Paper
Full-text available
In real applications normality of the errors is a routine assumption for the linear model, but it may be unrealistic. In fact often residuals exhibit non normal shape, with an heavy right or left tail. In this work, we relax the normality assumption by considering that the errors follow a beta skew-normal distribution. The new regression model repr...
Article
Full-text available
We consider a new generalization of the skew-normal distribution introduced by Azzalini (1985). We denote this distribution Beta skew-normal (BSN) since it is a special case of the Beta generated distribution (Jones, 2004). Some properties of the BSN are studied. We pay attention to some generalizations of the skew-normal distribution (Bahrami et a...
Conference Paper
Full-text available
In this paper we define and study a three-parameter distribution, referred to as the Beta skew-normal distribution (BSN), which is a generalization of the skew-normal distribution introduced by Azzalini in 1985. This family is obtained using the generator approach suggested by Eugene et al. (2002) and Jones (2004). Some properties of the proposed d...
Article
Full-text available
The skew-normal model is a class of distributions that extends the Gaussian family by including a skewness parameter. This model presents some inferential problems linked to the estimation of the skewness parameter. In particular its maximum likelihood estimator can be infinite especially for moderate sample sizes and is not clear how to calculate...
Article
Full-text available
The aim of this article is to introduce a new family of distributions, which generalizes the skew normal distribution (SN). This new family, called Beta skew-normal (BSN), arises naturally when we consider the distributions of order statistics of the SN. The BSN can also be obtained as a special case of the Beta generated distribution (Jones (2004)...
Article
Full-text available
In recent years, spatial and spatio-temporal modeling have become an important area of research in many fields (epidemiology, environmental studies, disease mapping). In this work we propose different spatial models to study hospital recruitment, including some potentially explicative variables. Interest is on the distribution per geographical unit...

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