Carlo Cavicchia

Carlo Cavicchia
Erasmus University Rotterdam | EUR · Department of Econometrics

Ph.D.
Assistant Professor

About

24
Publications
1,408
Reads
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37
Citations
Citations since 2017
24 Research Items
37 Citations
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Introduction
I am a statistician and my research is focused on the methodological and computational aspects of data analysis. The best feeling is when I can use my knowledge to solve practical and real problems. I am currently working on latent variable models, unsupervised classification and model-based composite indicators.
Additional affiliations
November 2016 - February 2020
Sapienza University of Rome
Position
  • PhD Student
Education
November 2013 - January 2016
Sapienza University of Rome
Field of study
  • Statistics
November 2009 - July 2013
Sapienza University of Rome
Field of study
  • Statistics

Publications

Publications (24)
Article
Full-text available
Hierarchical models are often considered to measure latent concepts defining nested sets of manifest variables. Therefore, by supposing a hierarchical relationship among manifest variables, the general latent concept can be represented by a tree structure where each internal node represents a specific order of abstraction for the latent concept mea...
Article
Gaussian Mixture Models (GMMs) are one of the most widespread methodologies for model-based clustering. They assume a multivariate Gaussian distribution for each component of the mixture, centered at the mean vector and with volume, shape and orientation derived by the covariance matrix. To reduce the large number of parameters produced by the cova...
Article
Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest possible part of the total variance of the observed variables. Several methodologies have been proposed either to improve the interpretation of PCA results (e.g., by means of orthogonal, oblique rotatio...
Article
Full-text available
In the cluster analysis literature, there are several partitioning (non-hierarchical) methods for clustering multivariate objects based on model estimation. Distinct to these methods is the use of a system of n nested statistical models and the optimization of a loss function to best-fit a clustering model to observed data. Many hierarchical cluste...
Article
In the last years, the quantity of information and statistics about waste management are more and more consistent but so far, few studies are available in this field. The goal of this paper is of producing a model-based Composite Indicator of “good” Waste Management, in order to provide a useful tool of support for EU countries’ policy-makers and i...
Preprint
Full-text available
The degree to which subjects differ from each other with respect to certain properties measured by a set of variables, plays an important role in many statistical methods. For example, classification, clustering, and data visualization methods all require a quantification of differences in the observed values. We can refer to the quantification of...
Conference Paper
Full-text available
In the last years, the production of information and statistics about waste management and separated waste collection has consistently increased. This paper builds a composite indicator for the separated waste collection in Italy taking into consideration both the performances and the costs via a hierarchical latent variable model. In detail, we pr...
Article
With the aim of assess the pollutant load from the Volturno River into the Mediterranean Sea, the amount of some heavy metals (As, Hg, Cd, Cr, Cu, Ni, Pb and Zn) was determined in water dissolved phase (DP), suspended particulate matter (SPM) and sediment samples. Total heavy metal concentrations were in the range 0.20-65.67 μg L −1 (mean value of...
Conference Paper
Full-text available
In the last years, the use of composite indicators has consistently increased , and the necessity to build model-based composite indicators with a strong methodological statistical approach becomes more and more important for reasons of trustworthiness. In this paper, we propose to build a composite indicators system able to measure different level...
Article
Full-text available
Online education is nowadays becoming increasingly important in students' learning process. To plan future activities within an online course, it is crucial to understand which factors mostly contribute to Student Satisfaction which, in turn, affects the students' performances. The data collected from students attending the course in Statistics of...
Conference Paper
Full-text available
Hierarchical relationships among manifest variables can be detected by analyzing their correlation matrix. To pinpoint the hierarchy underlying a multidi-mensional phenomenon, the Ultrametric Correlation Model (UCM) has been proposed with the aim of reconstructing a nonnegative correlation matrix via an ultra-metric one. In this paper, we illustrat...
Conference Paper
Full-text available
Complex multidimensional concepts are often explained by a tree-shape structure by considering nested partitions of variables, where each variable group is associated with a specific concept. Recalling that relations among variables can be detected by their covariance matrix, this paper introduces a covariance structure that reconstructs hierarchic...
Article
Full-text available
In the last years, with the data revolution and the use of new technologies, phenomena are frequently described by a huge quantity of information useful for making strategic decisions. A priority for policymakers is having simple statistical tools useful to synthesize data. Such tools are represented by composite indicators (CIs). According to the...
Conference Paper
Full-text available
The use of mobile phones while driving is one of the main causes of road accidents and it is an ever-growing phenomenon. The key aim of this study is to simultaneously analyze individual Knowledge, Attitudes, and Behaviors toward the use of mobile phones while driving in one of the largest and most populous metropolitan areas of Italy, Naples. The...
Conference Paper
Full-text available
Multidimensional phenomena are often characterised by nested latent concepts ordered in a hierarchical structure, from the most specific to the most general ones. In this paper, we model a nonnegative data covariance matrix by extending the Ultrametric Correlation Model to covariance matrices. The proposal is a parsimonious model which identifies a...
Article
Full-text available
A correction to this paper has been published: https://doi.org/10.1007/s40300-021-00209-6
Article
Full-text available
Teachers' performances also depend on whether and how they are satisfied with their job. Therefore, Teacher Job Satisfaction must be considered as the driver of teachers' accomplishments. To plan future policies and improve the overall teaching process, it is crucial to understand which factors mostly contribute to Teacher Job Satisfaction. A Commo...
Article
Many relevant multidimensional phenomena are defined by nested latent concepts, which can be represented by a tree-structure supposing a hierarchical relationship among manifest variables. The root of the tree is a general concept which includes more specific ones. The aim of the paper is to reconstruct an observed data correlation matrix of manife...
Chapter
Phenomena are usually multidimensional and their complexity cannot be directly explored via observable variables. For this reason, a hierarchical structure of nested latent concepts representing different levels of abstraction of the phenomenon under study may be considered. In this paper, we provide a comparison between a procedure based on hierar...
Conference Paper
Full-text available
Manifold multidimensional concepts are explained via a tree-shape structure by taking into account the nested hierarchical partition of variables. The root of the tree is a general concept which includes more specific ones. In order to detect the different specific concepts at each level of the hierarchy, we can identify two different features rega...
Conference Paper
Full-text available
Nowadays climate change is a crucial and compelling topic in public debate because of the risks for human life it could entail. The current warming trend is particularly significant and the entire scientific community warns governments to take action to reduce detrimental effects of the human activity. Although climate on Earth has changed througho...
Conference Paper
Full-text available
We are more than 7.5 billion people on our planet, and we are producing waste every day. Although the management of the waste keeps improving in the EU, many esti- mations tell us that half of that waste is not collected, treated or safely disposed of. That is why policymakers need consistent and useful tools to measure and monitor quality and effi...
Conference Paper
Full-text available
The development of new technologies and methods of data collection produces the necessity to summarise the large quantity of information that is available. Usually, we face a data matrix X of size (n × J), corresponding to n statistical units and J quantitative variables, where n and J are very large. Clustering is the analysis which identifies hom...

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