Zobrazeno 1 - 10
of 232
pro vyhledávání: '"Maturo Fabrizio"'
Autor:
Porreca, Annamaria, Ventre, Viviana, Martino, Roberta, Rambaud, Salvador Cruz, Maturo, Fabrizio
Classical finance models are based on the premise that investors act rationally and utilize all available information when making portfolio decisions. However, these models often fail to capture the anomalies observed in intertemporal choices and dec
Externí odkaz:
http://arxiv.org/abs/2410.16307
Autor:
Maturo, Fabrizio, Porreca, Annamaria
The positioning of this research falls within the scalar-on-function classification literature, a field of significant interest across various domains, particularly in statistics, mathematics, and computer science. This study introduces an advanced m
Externí odkaz:
http://arxiv.org/abs/2409.17804
Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble methods.Th
Externí odkaz:
http://arxiv.org/abs/2409.07879
Autor:
Maturo, Fabrizio, Porreca, Annamaria
This paper introduces a novel supervised classification strategy that integrates functional data analysis (FDA) with tree-based methods, addressing the challenges of high-dimensional data and enhancing the classification performance of existing funct
Externí odkaz:
http://arxiv.org/abs/2408.13179
Autor:
Maturo, Fabrizio, Porreca, Annamaria
The advent of big data has raised significant challenges in analysing high-dimensional datasets across various domains such as medicine, ecology, and economics. Functional Data Analysis (FDA) has proven to be a robust framework for addressing these c
Externí odkaz:
http://arxiv.org/abs/2408.12288
This paper introduces a Random Survival Forest (RSF) method for functional data. The focus is specifically on defining a new functional data structure, the Censored Functional Data (CFD), for dealing with temporal observations that are censored due t
Externí odkaz:
http://arxiv.org/abs/2407.15340
Publikováno v:
Int. J. Approx. Reasoning, 176 (2025), 109319.
Social network analysis (SNA) helps us understand the relationships and interactions between individuals, groups, organizations, or other social entities. In the literature, ties are generally considered binary or weighted based on their strength. No
Externí odkaz:
http://arxiv.org/abs/2407.02401
Publikováno v:
Stat Comput 34, 191 (2024)
Many conventional statistical and machine learning methods face challenges when applied directly to high dimensional temporal observations. In recent decades, Functional Data Analysis (FDA) has gained widespread popularity as a framework for modeling
Externí odkaz:
http://arxiv.org/abs/2403.15778
Autor:
Maturo, Fabrizio1 (AUTHOR) fabrizio.maturo@unicampania.it, Fortuna, Francesca2 (AUTHOR), Di Battista, Tonio3 (AUTHOR)
Publikováno v:
Annals of Operations Research. Nov2024, Vol. 342 Issue 3, p1547-1562. 16p.
Publikováno v:
In International Journal of Approximate Reasoning January 2025 176