In Progress

Research Area
Basic Research

Start Date
2021-09-01

End date
2024-08-31

Status
In progress

Project leaders
de Uña-Álvarez, Jacobo; Pardo Fernández, Juan Carlos

Description
Statistics is a fundamental piece in the progress of scientific knowledge, as it provides rigorous models and methods to analyse data and come to correct conclusions. The applications of the statistical procedures appear in almost any branch of knowledge: medicine, biology, engineering, economy, social sciences, etc. Nowadays, data are becoming more and more accesible, but also demand more sophisticated methods to be analysed. The role of a researcher in mathematical statistics consists of several scientific activities. First of all, the researcher develops new models and methods to deal with new problems, or alternative methods for existing ones. The rigorous mathematical treatment of these models and methods, the study of their theoretical properties and the analysis of their practical performance in simulation studies and in applications occupy a main part of our research activity. Second, the computational implementation of the methods and its dissemination amongst the scientific community is also done by the statistical researcher. Finally, the collaboration with researchers in other areas in order to provide statistical expertise in the application of the methods is also our duty. This project covers the three abovementioned aspects: development and study of new statistical models and methods, practical implementation and application to other areas. Statistical models range over a large set of possibilities, from very simple to extremely complicated. Normally, a compromise between several aspects such as simplicity, interpretability and flexibility is desirable. In this regard, nonparametric and semiparametric statistics are well positioned, as they are flexible enough and can reach good results in a large variety of situations without imposing unrealistic hypotheses to the models. Unlike, parametric statistics, where the objective is to estimate finite-dimensional parameters, the objective of nonparametric statistics is to estimate and perform inference about curves. Here, the term “curve” must be understood in a broad sense and includes density functions,cumulative distribution functions, regression functions, variograms, etc. This project focuses on the development of new models and methods in nonparametric and semiparametric statistics. Curve estimation and inference, specially testing procedures, form the core of the project. The rigorous analysis of the proposed methodologies, their practical implementation and their application are the cornerstones of this proposal. More specifically, this project contributes with new advances in methods for high-dimensional data, survival analysis, nonparametric regression, ROC curves, goodness-of-fit testing and testing hypotheses in regression models. Besides, applications to other areas and the elaboration of friendly-use code in R are also intended. This project will contribute in a deeper knowledge of methods in nonparametric and semiparametric statistics. The expected outcome is twofold: publications in specialized highstandard journals in the area of statistics and collaborations with researchers in other areas. The project is the natural continuation of four previously funded projects (MTM 2005, 2008, 2011 and 2014).

 

Finished

Research Area
Basic Research

Start Date
2018-01-01

End date
2020-12-31

Status
Finished

Project leaders
Rodríguez Álvarez, María Xosé and Lee, Dae-Jin (BCAM – Basque Center for Applied Mathematics)

Research Area
Basic Research

Start Date
2018-01-01

End date
2021-12-31

Status
Finished

Project leaders
Fiestras Janeiro, María Gloria

Members
Fiestras Janeiro, María Gloria; Quinteiro Sandomingo, Mª del Carmen; Sánchez Rodríguez, María Estela; Mosquera Rodríguez, Manuel Alfredo

Description
In this subproject we study various problems of game theory and optimization. The project is structured around two lines of research: 1. Solutions in game theory. 2. Applications of cooperative games in multi-agent operational research models. These lines entail six tasks that address basic problems of game theory. They are the following: 1.1. Solutions in cooperative games with transferable utility. 1.2. Solutions in cooperative games in partition function form. 1.3. Power indices. 1.4. Delegation and non-cooperative games. 2.1. Cooperation in sequencing problems. 2.2. Coalitional games with strategies and inventory management. Our research team consists of four doctors and one research trainee.

Research Area
Basic Research

Start Date
2018-01-01

End date
2020-12-31

Status
Finished

Project leaders
de Uña-Álvarez, Jacobo; Pardo Fernández, Juan Carlos

Description
Statistics is a fundamental piece in the progress of scientific knowledge, as it provides rigorous models and methods to analyse data and come to correct conclusions. The applications of the statistical procedures appear in almost any branch of knowledge: medicine, biology, engineering, economy, social sciences, etc. Nowadays, data are becoming more and more accesible, but also demand more sophisticated methods to be analysed. The role of a researcher in mathematical statistics consists of several scientific activities. First of all, the researcher develops new models and methods to deal with new problems, or alternative methods for existing ones. The rigorous mathematical treatment of these models and methods, the study of their theoretical properties and the analysis of their practical performance in simulation studies and in applications occupy a main part of our research activity. Second, the computational implementation of the methods and its dissemination amongst the scientific community is also done by the statistical researcher. Finally, the collaboration with researchers in other areas in order to provide statistical expertise in the application of the methods is also our duty. This project covers the three abovementioned aspects: development and study of new statistical models and methods, practical implementation and application to other areas. Statistical models range over a large set of possibilities, from very simple to extremely complicated. Normally, a compromise between several aspects such as simplicity, interpretability and flexibility is desirable. In this regard, nonparametric and semiparametric statistics are well positioned, as they are flexible enough and can reach good results in a large variety of situations without imposing unrealistic hypotheses to the models. Unlike, parametric statistics, where the objective is to estimate finite-dimensional parameters, the objective of nonparametric statistics is to estimate and perform inference about curves. Here, the term “curve” must be understood in a broad sense and includes density functions,cumulative distribution functions, regression functions, variograms, etc. This project focuses on the development of new models and methods in nonparametric and semiparametric statistics. Curve estimation and inference, specially testing procedures, form the core of the project. The rigorous analysis of the proposed methodologies, their practical implementation and their application are the cornerstones of this proposal. More specifically, this project contributes with new advances in methods for high-dimensional data, survival analysis, nonparametric regression, ROC curves, goodness-of-fit testing and testing hypotheses in regression models. Besides, applications to other areas and the elaboration of friendly-use code in R are also intended. This project will contribute in a deeper knowledge of methods in nonparametric and semiparametric statistics. The expected outcome is twofold: publications in specialized highstandard journals in the area of statistics and collaborations with researchers in other areas. The project is the natural continuation of four previously funded projects (MTM 2005, 2008, 2011 and 2014).

Research Area
Basic Research

Start Date
2012-01-01

End date
2014-12-31

Status
Finished

Project leaders
Fiestras Janeiro, Gloria

Members
Fiestras Janeiro, Gloria; Mosquera Rodríguez, Manuel Alfredo; Sánchez Rodríguez, María Estela.

Description

Research Area
Basic Research

Start Date
2009-01-01

End date
2012-12-31

Status
Finished

Project leaders
de Uña-Álvarez, Jacobo

Members
de Uña-Álvarez, Jacobo; Pardo Fernández, Juan Carlos; Iglesias Pérez, María del Carmen; Cotos Yáñez, Tomás R.; Álvarez Díaz, Marcos; Costa da Conceiçao Amorim, Ana Paula; Liang, Han-Ying; Gonçalves de Macedo Moreira, Carla; Veraverbeke, Noel; Rodríguez Girondo, Mar.

Description
Nonparametric and semiparametric methods have become a flexible statistical tool in exploratory data analysis and inference, including: curve estimation, goodness-of-fit testing, regression analysis, resampling methods, multivariate statistics, time series forecasting, or spatial data analysis. This project includes a number of methodological advances in this area. The motivation comes from specific real life problems for which proper statistical tools are missing. Explicitly, one of our main focus will be solving nonstandard issues in the statistical analysis of survival data coming from medical sciences (similar examples being found in the econometric analysis of duration or transition data, and in reliability studies). Non-Markov multi-state models, multivariate survival analysis, models for bivariate censoring, singly or doubly truncated data, censored and/or truncated dependent data, dimension reduction methods, location-scale models, additive censored regression, models for dependent censoring, or presmoothing methods for informative censoring, are some of the specific, modern problems we address. Also, in the field of engineering and environmental sciences, one often has to face large data sets showing a complex pattern of spatial (or time-space) dependence. Our plan is concerned with the development and application of new flexible tools in this context, including nonparametric variogram estimation, kriging, expert systems design, or classification methods, under the useful view of fuzzy sets theory. As a third example, we consider time series forecasting in finance, tourism and the environment, via nonlinear and nonparametric methods. Some techniques which have been deeply investigated during the recent years and new advances will be used to this end. We include as a task the development of new software to implement the new proposed methods. To this regard, this project promotes the using of R as a free programming and data analysis language, so any applied scientist will have access to the routines we will generate

Research Area
Basic Research

Start Date
2005-12-31

End date
2008-12-31

Status
Finished

Project leaders
de Uña-Álvarez, Jacobo

Members
de Uña-Álvarez, Jacobo; Saavedra González, Ángeles; Iglesias Pérez, María del Carmen; Cotos Yáñez, Tomás R.; Álvarez Díaz, Marcos; Costa da Conceiçao Amorim, Ana Paula; Liang, Han-Ying; Gonçalves de Macedo Moreira, Carla.

Description
Applied models to medicine, economy, finance, engineering and environment

Research Area
Basic Research

Start Date
2012-01-01

End date
2016-12-31

Status
Finished

Project leaders
de Uña-Álvarez, Jacobo

Description
Mathematical statistics provides support for data-based decisions in a variety of fields such as (bio)medical sciences, actuarial sciences, financial mathematics, biology, bioinformatics, engineering, etc. The current research questions in statistics require highly advanced techniques, they are needed to understand and to deal with the contemporary issues such as high or ultra-high dimensional data, functional or spatial data, and many sorts of ‘nonperfect’ data (data measured with error, incomplete data, censored or truncated data). Important research topics are: the development of estimators and tests in high-dimensional settings; dimension reduction; clustering and classification methods; the study of flexible non- and semiparametric models; goodness-of-fit tests and diagnostics for complex models; a study of dependence structures. Specific emphasis will be put on measurement error and inverse problems and on survival data. Moving beyond the current stage of knowledge requires a combination of theoretical skills. Therefore a group of national international researchers will join forces in this scientific network. This network is coordinated by Gerda Claeskens, Katholieke Universiteit Leuven.

Project’s website

Research Area
Basic Research

Start Date
2011-01-01

End date
2013-12-31

Status
Finished

Project leaders
de Uña-Álvarez, Jacobo

Members
de Uña-Álvarez, Jacobo; Pardo Fernández, Juan Carlos; Liang, Han-Ying; Iglesias Pérez, María del Carmen; Roca Pardiñas, Javier

Description
In this project up to six basic research lines in statistical mathematics are planned, related to new methods and/or new results in semiparametric and nonparametric estimation and inference techniques. These are:

New results in nonparametric curve estimation with dependent data, under censoring and truncation conditions.
Advances in high-dimensional censored regression.
Advances in nonparametric estimation and inference in multi-state models.
New goodness-of-fit tests based on smoothing.
Advances in ROC curves.
New results and applications in local polynomial regression and GAM.
All these methodological advances are oriented to solve specific problems arising from biomedical sciences, economy, and biology.

Research Area
Basic Research

Start Date
2015-01-01

End date
2017-12-31 (2018-12-31 extension)

Status
Finished

Project leaders
Fiestras Janeiro, María Gloria

Members
Fiestras Janeiro, María Gloria; Mosquera Rodríguez, Manuel Alfredo; Sánchez Rodríguez, Maria Estela; Quinteiro Sandomingo, Mª del Carmen

Description
In this project we study several optimization and cost allocation problems arising in decision situations in which several agents interact. In our study we use models and techniques taken from cooperative game theory and mathematical programming. The project is structured around eight tasks, three of which belong to the field of cooperative game theory (1-3). The other five treat problems with components taken from optimization theory and cooperative game theory (4-8). They are: 1. Axiomatic characterizations and computation of solutions in cooperative games with exogenous constraints. 2. Study of power indices with exogenous constraints. 3. Study of some geometrical aspect of cooperative games. 4. Linear production games with externalities. 5. Optimization and allocation in minimum cost arborescence problems. 6. Optimization and allocation in multi-agent routing problems. 7. Optimization and allocation in multi-agent fire fighting problems. 8. Optimization and allocation in multi-agent inventory management.

Research Area
Basic Research

Start Date
2012-01-01

End date
2015-12-31

Status
Finished

Project leaders
de Uña-Álvarez, Jacobo

Description
MEDIASRES is an international PhD programme for highly motivated young scientists, where state-of-the-art research is combined with a comprehensive training programme. The scientific network goal is to develop tailor-made statistical methodology for meeting specific current and future challenges in diagnostic, prognostic and therapeutic research and systematic reviews. MEDIASRES is coordinated by the University Medical Center Freiburg (notably Professor Martin Schumacher from the Institute of Medical Biometry and Medical Informatics), has started on January 1, 2012 and will be carried out over a period of 4 years. The network consists of seven academic partner institutions and three partners from industry.

Project’s website

Research Area
Basic Research

Start Date
2012-01-01

End date
2014-12-31

Status
Finished

Project leaders
de Uña-Álvarez, Jacobo

Members
de Uña-Álvarez, Jacobo; Pardo Fernández, Juan Carlos; Liang, Han-Ying; Iglesias Pérez, María del Carmen; Roca Pardiñas, Javier; Saavedra, A.; Cotos Yáñez, Tomás R.; Moreira, Carla; Martínez Camblor, Pablo; Franco Pereira, Alba; Rodríguez Girondo, Mar; Sestelo Pérez, Marta; Amorim, Ana Paula; Mendonça, J.

Description
The focus of this project is on the development of nonparametric and semiparametric (i.e., flexible) statistical methods for estimation and testing problems. Covered topics include: survival analysis, multi-state models, censored and truncated data, length-bias, location-scale models, ROC curves, variable selection, k-sample tests, high-dimensional hypotheses testing, dependent data, reliability, significance on derivatives and change points, bootstrap methods, sampling theory in nonstandard settings, spatial data, GAMs, copula functions, or missing data. Applications include: biology, biomedicine, health, economics, finance, engineering, environmental sciences, or sports. The wideness of the project is a consequence of the size of the group (10 Ph. D.) and its strong background in mathematical statistics and experience in applications and transfer of knowledge, specially to the biomedical area. Indeed, collaboration with medical centers such as the Hospital Universitario Central de Asturias (HUCA, Oviedo), the Complexo Hospitalario de Vigo (CHUVI, Vigo), the Complexo Hospitalario de Ourense (CHOU, Ourense), and the Portuguese Institute of Oncology (IPO, Porto) is planned. Also, in the analysis of the dynamics and the exploitation of marine populations, we will collaborate with CETMAR (Centro Tecnológico del Mar, Vigo), Centro Oceanográfico de Vigo (IEO), and the Instituto de Hidráulica Ambiental at the University of Cantabria (Santander).