Statistics and Risk Management

NOVA Math > Research Groups > Statistics and Risk Management

Statistics
and Risk Management

About

The Statistics and Risk Management group activity covers a wide range of subjects that are encompassed in three main research lines: Statistical Inference, Distribution Theory and Actuarial and Financial Mathematics. Within Statistical Inference we study various extensions of linear models with special focus in analysis of variance for models with a random number of observations. Concerning Distribution Theory there are two main research areas: near-exact distributions for likelihood ratio test statistics and extreme value theory. In Actuarial and Financial Mathematics we spread over the classical actuarial problems in risk theory, to new models for pricing and hedging derivative products, passing through Markov chain models applications to credit scoring and insurance bonus-malus models.

Frederico Almeida Gião Gonçalves Caeiro

Statistics and Risk Management Coordinator

Team

Publications

Addressing the algebraic structure of a linear mixed model with balanced design towards model extension
2024, MATHEMATICAL METHODS IN THE APPLIED SCIENCES, Santos,C;Dias,C;Brites,NM;Nunes,C;Mexia,JT
An Interactive Dashboard for Statistical Analysis of Intensive Care Unit COVID-19 Data
2024, BioMedInformatics, Dias,R;Ferreira,A;Pinto,I;Geraldes,C;Von Rekowski,C;Bento,L
Biadditive models: Commutativity and optimum estimators
2024, COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, Alexandre,A;Oliveira,M;Garcao,E;Mexia,J
Comparison of optimal harvesting policies with general logistic growth and a general harvesting function
2024, MATHEMATICAL METHODS IN THE APPLIED SCIENCES, Reis,M;Brites,NM;Santos,C;Dias,C
Contributing with private bundles to public goods
2024, ECONOMIC THEORY, Faias,M;Guevara Velazquez,M;Moreno Garcia,E
Estimation-Calibration of Continuous-Time Non-Homogeneous Markov Chains with Finite State Space
2024, MATHEMATICS, Esquivel,ML;Krasii,NP;Guerreiro,GR
Fisheries Inspection in Portuguese Waters from 2015 to 2023
2024, SCIENTIFIC DATA, Moura,R;Santos,NP;Vala,A;Mendes,L;Simoes,P;Neto,MD;Lobo,V
Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
2024, BIOINFORMATICS AND BIOLOGY INSIGHTS, Vieira,FG;Bispo,R;Lopes,MB
Long-term in situ Eulerian Sea surface temperature records along the Portuguese Coast
2024, DATA IN BRIEF, Santos,NP;Moura,R;da Silva,CS;Lamas,L;Lobo,V;Neto,MD
Marginalized LASSO in the low-dimensional difference-based partially linear model for variable selection
2024, JOURNAL OF APPLIED STATISTICS, Norouzirad,M;Moura,R;Arashi,M;Marques,FJ

Projects

HEATMan – Gestão do calor em equipas NRBQ
Exército Português – CINAMIL, Paula Simões
Link Me Up – 1000 ideias
ERASMUS+/União Europeia, Cristina Dias
MoSBurn: Modeling the multifactorial burnout syndrome in college students
FCT - Fundação para a Ciência e a Tecnologia, I.P., Luís Miguel Grilo
OMNI BEAST
Project n.: 2018-1-PL01-KA203-051137 Erasmus + Programme Key Action 2: Strategic Partnerships for higher...
PerMediK: Personalized medicine in chronic kidney disease: improved outcome based on Big Data
COST - European Cooperation in Science and Technology (EU), Marta Belchior Lopes
PrISAEx-Proteção de Infraestruturas Sujeitas a Ações Extremas
Exército Português – CINAMIL, Paula Simões
SHIFT - Sustainability-oriented, Highly-interactive and Innovation-based Framework for Tourism Marketing
FCT - Fundação para a Ciência e a Tecnologia, I.P., Sandra Nunes
SmartVest - Vestuário inteligente para monitorização em tempo real dos parâmetros fisiológicos das Equipas NBQR
Exército Português – CINAMIL, Paula Simões
VOOmics: OMICs approaches to reveal the anticancer properties of Virgin Olive Oil
FCT - Fundação para a Ciência e a Tecnologia, I.P., Marta Belchior Lopes
“OMNI - BE Aware STudent”
ERASMUS+, Cristina Dias

Software

 
    • Sá Ferreira M, Bispo R (2023). RWgraph: Random Walks on Graphs Representing a Transactional Network. R package version 1.0.0, https://CRAN.R-project.org/package=RWgraph
    • Caeiro F,  Mateus A (2022). randtests: Testing Randomness in R. R package version 1.0.1, https://CRAN.R-project.org/package=randtests
    • a computational module with the implementation of the exact distribution and approximations for the Bartels randomness test statistic: see Appendix
    • a package with the implementation of the main threshold selection methods: see here