About
24
Publications
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323
Citations
Introduction
Carmela Iorio currently works at the Department of Industrial Engineering, University of Naples Federico II. Carmela does research in Statistics, Probability Theory and Artificial Intelligence. Their most recent publication is 'A P-Spline based clustering approach for portfolio selection.'
Publications
Publications (24)
Ensemble methods such as random forests have transformed the landscape of supervised learning, offering highly accurate prediction through the aggregation of multiple weak learners. However, despite their effectiveness, these methods often lack transparency, impeding users' comprehension of how RF models arrive at their predictions. Explainable ens...
Nowadays, statistical arbitrage is one of the most attractive fields of study for researchers, and its applications are widely used also in the financial industry. In this work, we propose a new approach for statistical arbitrage based on clustering stocks according to their exposition on common risk factors. A linear multifactor model is exploited...
Ensemble methods are supervised learning algorithms that provide highly accurate solutions by training many models. Random forest is probably the most widely used in regression and classification problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression. However, such...
When we published the article, the affiliation of the 3rd and 4th author were mixed-up. Antonio D’Ambrosio is affiliated at Department of Economics and Statistics, and Roberta Siciliano is affiliated at Department of Electrical and Information Technology, both at the University of Naples Federico II. The original publication has been corrected.
In this paper, we present a portfolio optimization strategy based on a novel approach in assets clustering on the financial background of the Arbitrage Pricing Theory, a well-known multi-factor model. In particular, our aim is to exploit data analysis tools, such as the techniques of features extraction and feature selection, to group assets that e...
Even if large historical dataset could be available for monitoring key quality features of a process via multivariate control charts, previous knowledge may not be enough to reliably identify or adopt a unique model for all the variables. When no specific parametric model turns out to be appropriate, some alternative solutions should be adopted and...
Every time some judges are asked to express their preferences on a set of objects we deal with ranking data. Nowadays, the analysis of such data arise in many scientific fields of science, such as computer sciences, social sciences. Political sciences, medical sciences, just to cite a few. For this reason, the interest in rank aggregation problem i...
Knowledge discovery from various sources of information based on different data types for decision and accurate prediction can be rather complex and costly without a statistical information system. In Big Data Era, Statistical Tourism Observatory needs to be revised. This paper introduces a conceptual model of Digital Tourism System (DTS) where var...
Portfolios constructed by the classical mean-variance model are very sensitive to outliers. We propose the use of a non-parametric estimation method based on statistical data depth functions. Specifically, we exploit the notion of the weighted \(L^{p}\) depth function to obtain robust estimates of the mean and covariance matrix of the asset returns...
Objectives
A systematic bibliometric analysis was performed to investigate trends in Complex Oral Sensitivity Disorder (COSD) research worldwide and compare the contributions of different countries/institutions, scientific journals, authors, keywords, and citations.
Methods
Web of Science database from 1985 to 2018 was systematically searched to i...
The rank aggregation problem can be summarized as the problem of aggregating individual preferences expressed by a set of judges to obtain a ranking that represents the best synthesis of their choices. Several approaches for handling this problem have been proposed and are generally linked with either axiomatic frameworks or alternative strategies....
The aim of this study is to provide visual pruning and decision tree selection for classification and regression trees. Specifically, we introduce an unedited tree graph to be made informative for recursive tree data partitioning. A decision tree is visually selected through a dendrogram-like procedure or through automatic tree-size selection. Our...
Partial Least Squares Path Modeling is suitably defined and applied in a research field in the largest public research organization in Italy, namely the National Research Council (CNR). In literature studies on Project Management (PM) mostly cover the industry sector rather than the world of science and research. A model with theoretical constructs...
In the last years, many clustering techniques dealing with time course data have been proposed due to recent interests in studying phenomena that change over time. A new clustering method suitable for time series applications has been recently proposed by exploiting the properties of the P-splines approach. This semi-parametric tool has several adv...
This deliverable illustrates the analytical framework developed and used in MAGIC, that is,
Quantitative Story-Telling (QST) based on the accounting method Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM). The various documents composing this deliverable
are intended for use both within the consortium and beyond. As r...
In recent years the analysis of preference rankings has become an increasingly important topic. One of the most important tasks in dealing with preference rankings is the identification of the median ranking, namely that ranking that best represents the preferences of a population of judges. This task is known
with several alternative names, such a...
In the framework of regression trees, this paper provides a recursive partitioning methodology to deal with a non-standard response variable. Specifically, either multivalued numerical or modal response of the type histogram will be considered. These data are known as symbolic data, which special cases are classical data, imprecise data, conjunctiv...
We introduce a parsimonious model-based framework for clustering time course data. In these applications the computational burden becomes often an issue due to the large number of available observations. The measured time series can also be very noisy and sparse and an appropriate model describing them can be hard to define. We propose to model the...
In comparing clustering partitions, Rand index (RI) and Adjusted Rand index
(ARI) are commonly used for measuring the agreement between the partitions.
Both these external validation indexes aim to analyze how close is a cluster to
a reference (or to prior knowledge about the data) by counting corrected
classified pairs of elements. When the aim is...
We introduce a parsimonious model-based framework for clustering time course
data. In these applications the computational burden becomes often an issue due
to the number of available observations. The measured time series can also be
very noisy and sparse and a suitable model describing them can be hard to
define. We propose to model the observed...
Fuzzy clustering methods allow the objects to belong to several clusters
simultaneously, with different degrees of membership. However, a factor that
influences the performance of fuzzy algorithms is the value of fuzzifier
parameter. In this paper, we propose a fuzzy clustering procedure for data
(time) series that does not depend on the definition...
The analysis of ranking data has recently received increasing attention in many fields (i.e. political sciences, computer sciences, social sciences, medical sciences, etc.). Typically when dealing with preference rankings one of the main issue is to find a ranking that best represents the set of input rankings. Among several measures of agreement p...
The most common approach to build a decision tree is based on a two-step procedure: growing a full tree and then prune it back. The goal is to identify the tree with the lowest error rate. Alternative pruning criteria have been proposed in literature. Within the framework of recursive partitioning algorithms by tree-based methods, this paper provid...