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

Endogenous formation of security exchanges
2017, Economic Theory, Faias,M;Luque,J
High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production
2017, JOURNAL OF INDUSTRIAL MICROBIOLOGY & BIOTECHNOLOGY, Sampaio,PN;Sales,KC;Rosa,FO;Lopes,MB;Calado,CRC
Joining models with commutative orthogonal block structure
2017, LINEAR ALGEBRA AND ITS APPLICATIONS, Santos,C;Nunes,C;Dias,C;Mexia,JT
Estimation in mixed models through three step minimization
2017, COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, Ferreira,D;Ferreira,SS;Nunes,C;Mexia,JT
The exact and near-exact distributions for the statistic used to test the reality of covariance matrix in a complex normal distribution
2017, Springer Proceedings in Mathematics and Statistics, Grilo,LM;Coelho,CA
Credit market segmentation, essentiality of commodities, and supermodularity
2017, JOURNAL OF MATHEMATICAL ECONOMICS, Faias,M;Pablo Torres Martinez,JP
Open markov chain scheme models fed by second order stationary and non stationary processes
2017, Revstat Statistical Journal, Esquível,ML;Guerreiro,GR;Fernandes,JM
INFERENCE FOR MULTIVARIATE REGRESSION MODEL BASED ON SYNTHETIC DATA GENERATED UNDER FIXED-POSTERIOR PREDICTIVE SAMPLING: COMPARISON WITH PLUG-IN SAMPLING
2017, REVSTAT-STATISTICAL JOURNAL, Moura,R;Klein,M;Coelho,CA;Sinha,B
Metabolic profiling of recombinant Escherichia coli cultivations based on high-throughput FT-MIR spectroscopic analysis
2017, BIOTECHNOLOGY PROGRESS, Sales,KC;Rosa,F;Cunha,BR;Sampaio,PN;Lopes,MB;Calado,CRC
Identifying top relevant dates for implicit time sensitive queries
2017, INFORMATION RETRIEVAL JOURNAL, Campos,R;Dias,G;Jorge,AM;Nunes,C

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