arXiv subjects

Roberta Pappadà

Publications and source records attributed to Roberta Pappadà.

5 recordsLinked to original sources

arXiv2025

Copula-Based Clustering of Financial Time Series via Evidence Accumulation

Understanding the dependence structure of asset returns is fundamental in risk assessment and is particularly relevant in a portfolio diversification strategy. We propose a clustering approach where evidence accumulated in a multiplicity of classifications is achieved using classical hierarchical procedures and multiple copula-based dissimilarity measures. Assets that are grouped in the same cluster are such that their stochastic behavior is similar during risky scenarios, and riskaverse investors could exploit this information to build a risk-diversified portfolio. An empirical demonstration of such a strategy is presented by using data from the EURO STOXX 50 index.

stat.AP↗

arXiv2025

Discrimination in machine learning algorithms

Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (like sex or race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases.

stat.ML↗

arXiv2021

pivmet: Pivotal Methods for Bayesian Relabelling and k-Means Clustering

The identification of groups' prototypes, i.e. elements of a dataset that represent different groups of data points, may be relevant to the tasks of clustering, classification and mixture modeling. The R package pivmet presented in this paper includes different methods for extracting pivotal units from a dataset. One of the main applications of pivotal methods is a Markov Chain Monte Carlo (MCMC) relabelling procedure to solve the label switching in Bayesian estimation of mixture models. Each method returns posterior estimates, and a set of graphical tools for visualizing the output. The package offers JAGS and Stan sampling procedures for Gaussian mixtures, and allows for user-defined priors' parameters. The package also provides functions to perform consensus clustering based on pivotal units, which may allow to improve classical techniques (e.g. k-means) by means of a careful seeding. The paper provides examples of applications to both real and simulated datasets.

stat.CO↗

arXiv2016

Maxima Units Search (MUS) algorithm: methodology and applications

An algorithm for extracting identity submatrices of small rank and pivotal units from large and sparse matrices is proposed. The procedure has already been satisfactorily applied for solving the label switching problem in Bayesian mixture models. Here we introduce it on its own and explore possible applications in different contexts.

stat.CO↗

arXiv2016

Relabelling in Bayesian mixture models by pivotal units

In this paper a simple procedure to deal with label switching when exploring complex posterior distributions by MCMC algorithms is proposed. Although it cannot be generalized to any situation, it may be handy in many applications because of its simplicity and very low computational burden. A possible area where it proves to be useful is when deriving a sample for the posterior distribution arising from finite mixture models when no simple or rational ordering between the components is available.

stat.CO↗