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Andrea Meilán-VilaView profile

Assistant Professor

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +3 more

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

José E. ChacónView profile

Professor

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +3 more

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.

• Non-parametric Inference
• Regression Models
• Missing Data Analysis
• +2 more

Ana Perez Gonzalez is a researcher at the Department of Statistics and Operational Research, Faculty of Business Sciences and Tourism, University of Vigo. She earned her PhD from the University of Santiago de Compostela in 2003, focusing on non-parametric inference for regression models with missing responses. Her research centers on robust statistical methods for missing data, functional regression, and non-parametric estimation. Key collaborations with Wenceslao Gonzalez Manteiga, Graciela Boente, and Ana Bianco Contributions to wild bootstrap calibration, bandwidth selection, and imputation techniques Her work addresses applications in biomedical statistics, climate modeling, and spectrometric studies. Notable methodologies include goodness-of-fit tests for missing data models, principal component-based estimators, and asymptotic variance analysis.

• Numerical Analysis
• Approximation Theory
• Multiresolution Analysis
• +7 more

Francesc Arandiga Llau is a Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with the ANIMS (Numerical Analysis, Images, Multiresolution and Simulation) research group, where he conducts research in applied mathematics with a focus on numerical methods and their applications. Education: PhD from Universitat de València (1992), thesis on operator approximation and spectral radius continuity, supervised by Dr. Vicent Caselles Costa. His research interests center on Numerical Analysis , Approximation Theory , and Multiresolution Methods , with significant contributions to WENO schemes , nonlinear interpolation , and image and signal compression . His work often bridges theoretical developments with practical implementations in computational mathematics and engineering. He has made notable advances in the stability, accuracy, and adaptability of reconstruction techniques for piecewise smooth and discontinuous functions. The analysis of his recent publications reveals a consistent focus on high-order numerical methods, particularly in the context of image processing and data compression . His work leverages multiresolution analysis , radial basis functions , and adaptive interpolation to improve accuracy and efficiency. Themes across his articles include monotonicity preservation, error control, and the design of nonlinear schemes that avoid spurious oscillations near discontinuities. There are no scientific awards explicitly mentioned in the provided text. Francesc Arandiga has extensive collaborative research, particularly with scholars such as Rosa Donat, Dionisio F. Yáñez, Pep Mulet, and Antonio Baeza. His work has been supported through various research projects, though specific grants are not detailed in the text. He has advised students, including those who have completed theses under his supervision, although a full list is not provided. He is a key member of the ANIMS research group, which focuses on Numerical Analysis, Images, Multiresolution, and Simulation. This team works on developing and analyzing advanced computational methods for scientific and engineering applications, particularly in the areas of data representation, image processing, and numerical solutions to differential equations.

• Quantile Regression
• Statistical Inference
• Biostatistics
• +4 more

Mercedes Conde Amboage is a Researcher at the University of Santiago de Compostela's Faculty of Mathematics, affiliated with the Department of Statistics, Mathematical Analysis and Optimization. She holds a PhD in Statistics (2017) from the same institution, focusing on Statistical Inference in Quantile Regression Models , advised by Dr. Wenceslao González Manteiga and Dr. César Andrés Sánchez Sellero. Her research interests center on quantile regression methodologies, statistical inference, and their applications in biomedical and environmental data. She contributes to the Galician Mathematical Research and Technology Center (CITMAga) and collaborates with research groups like GRID-BDS (Biostatistics and Biomedical Data Science) and MODESTYA (Optimization and Statistical Models). Her work bridges theoretical statistics with practical applications in clinical trials, proteomics, and environmental monitoring. Recent publications focus on quantile regression innovations in censored data analysis, biomedical biomarker identification, and environmental toxicology. She develops statistical software tools like the 'BwQuant' R package and has contributed to meta-analyses in dentistry and oncology. Notable collaborations include work on medication-related osteonecrosis of the jaw, HER2-positive breast cancer responses, and systematic reviews of surgical techniques. Her methodological advancements address high-dimensional covariates and lack-of-fit testing in complex models.

• Statistics
• Nonparametric Estimation
• Set Estimation
• +4 more

Alberto Rodriguez Casal is a Professor in the Department of Statistics, Mathematical Analysis and Optimization at the Faculty of Mathematics, University of Santiago de Compostela. He is affiliated with the Galician Mathematical Research and Technology Center (CITMAga) and leads the MODESTYA research group focused on Optimization, decision, statistical models and applications. His research primarily focuses on nonparametric statistics, with special emphasis on: Set and boundary estimation Density level sets and shape analysis Statistical methods for geometric inference Nonparametric estimation under shape constraints Analysis of his publication record spanning nearly two decades shows a consistent focus on geometric and shape-related statistical problems. His work bridges theoretical statistical developments with practical applications in areas such as environmental statistics and spatial analysis. Recent publications demonstrate continued innovation in data-driven methods for shape and boundary estimation, particularly through the application of alpha-shapes and other computational geometry approaches to statistical problems. Professor Rodriguez Casal has established significant collaborative networks, particularly with researchers including Saavedra-Nieves, Paula; Crujeiras, Rosa María; de Uña-Álvarez, Jacobo; Pateiro-López, Beatriz; and Cuevas, Antonio. His work has been published in top statistical journals including The Annals of Statistics, Journal of the American Statistical Association, Test, and Journal of Nonparametric Statistics, with over 25 publications since 2004 that have been cited more than 300 times.

Philipp SibbertsenView profile

Professor

• Time Series with Long Memory
• Non-linear Time Series Econometrics
• Statistics on Financial Markets
• +2 more

Professor Philipp Sibbertsen is a distinguished faculty member at the Institute of Statistics within the Faculty of Economics and Management at Leibniz University Hannover. He holds the position of Professor of Statistics since 2005 and has maintained an active research and teaching career throughout his academic journey. Professor of Statistics at Leibniz University Hannover (since 2005) Visiting Professor at CREATES, Aarhus University, Denmark (2011) Heisenberg Fellow of the Deutsche Forschungsgemeinschaft (DFG) (2004-2005) Research stay at Cardiff Business School, Cardiff University, UK (2001-2002) University Assistant at Technical University of Dortmund (2001-2004) Professor Sibbertsen's research primarily focuses on time series analysis with emphasis on long memory processes, nonlinear time series econometrics, and financial market statistics. His work bridges theoretical statistical methods with practical applications in economics and finance, particularly in the areas of fractional integration, cointegration, structural breaks, and volatility modeling. His research has significant implications for understanding market dynamics, forecasting economic trends, and developing robust statistical methodologies for complex data structures. His extensive publication record demonstrates a consistent focus on long memory processes and their applications across various domains. Recent publications (2021-2025) show his work expanding into climate data analysis, carbon pricing mechanisms, coastal risk assessment, and medical statistics, while maintaining his core expertise in time series econometrics. His research shows a strong pattern of interdisciplinary collaboration, with publications spanning economics, finance, environmental science, and medical research. Heisenberg Fellowship from DFG (2004-2005) Member of the Editorial Board of 'Statistical Papers' and 'ASta' Member of the Board of the German Statistical Society (since 2016) Professor Sibbertsen has held significant administrative roles including Spokesperson for the research focus 'Innovation and Learning' at Leibniz University Hannover (since 2018), Dean of Studies at the Faculty of Business and Economics (2011-2013), and Chairman of the Committee for Statistical Theory and Methodology of the German Statistical Society (2008-2012). His teaching portfolio includes Descriptive Statistics, Inductive Statistics, Advanced Statistics, and Time Series Analysis. While specific details about his advisees are not provided in the available materials, his extensive publication record suggests active mentorship of graduate students in statistics and econometrics.

• Statistics
• Bioinformatics
• Epidemiology
• +3 more

Jose Ameijeiras Alonso is a Professor at the University of Santiago de Compostela, serving as General Coordinator and Coordinator within the Faculty of Mathematics. He holds a Ph.D. in Statistics and Operations Research and is affiliated with the Department of Statistics, Mathematical Analysis and Optimization. His research focuses on statistical methodologies with applications in epidemiology, bioinformatics, and environmental science. Key areas include circular data analysis, nonparametric estimation, and multimodality testing. He has developed statistical tools for analyzing protein structures, wildfire patterns, and disease outbreaks such as COVID-19. Notable contributions include the R package Multimode for mode assessment and the SPQEIR model for evaluating epidemic control strategies. His work bridges theoretical statistics with real-world applications in healthcare and environmental management.

José E. ChacónView profile

Professor

• Nonparametric Statistics
• Kernel Smoothing Methods
• Density Estimation
• +3 more

José E. Chacón is a Professor of Statistics in the Department of Mathematics at the University of Extremadura since 2022. Previously, he held roles at the University of Valladolid and other positions within Extremadura. His research focuses on nonparametric statistics, kernel smoothing methods for density estimation, and their applications in clustering. He is actively involved in initiatives like the NICDA Workshop and Statistics Reading Club. Education history details are not explicitly provided in the text. His research interests emphasize advanced statistical methodologies including density derivative estimation, nonparametric clustering, and applications in epidemiology and ecology. Notable contributions include work on Bayesian taut splines, mode-based symmetry estimation, and statistical modeling for animal home ranges. Recent publications (2020–2024) explore cutting-edge topics such as geodesic normal distributions, cross-validation techniques, and SIR modeling for pandemic data. His work bridges theoretical advancements with practical applications in health, ecology, and machine learning. Awards and grants are not explicitly mentioned. Chacón has advised no listed students but collaborates on interdisciplinary projects, including the special issue on Data Science for COVID-19. His research lab focuses on multivariate kernel smoothing and nonparametric methods.

Rebeca PeláezView profile

Assistant Professor

• Credit Risk
• Nonparametric Estimation
• Bootstrap Methods
• +5 more

Rebeca Peláez is an Assistant Professor at the Department of Statistics, Universidad Carlos III de Madrid since 2023. She also holds a Quant position at Twinco Capital, having moved to industry in 2025. Her academic career includes a PhD in Statistics and Operations Research from Universidade da Coruña and a bachelor's degree in Mathematics from Universidad de Oviedo. Educational Background: Bachelor's in Mathematics, Universidad de Oviedo PhD in Statistics and Operations Research, Universidade da Coruña (2022), with a research stay at KU Leuven's Department of Economics. Her research interests focus on advancing credit risk measurement methodologies. Key areas include nonparametric estimation of probability of default, kernel smoothing, survival analysis, and mixture cure models. She also applies bootstrap techniques to contexts like electricity demand forecasting and public health analysis, such as modeling hospital length-of-stay during pandemics. Recent articles highlight her work on functional data analysis for energy systems, survival analysis applications, and refined nonparametric models for financial risk. These studies emphasize methodological innovation with practical implications in both academia and industry. She received the Extraordinary PhD Award from Universidade da Coruña in 2022 for her thesis on credit risk estimation. No current advisees or grants are listed in the provided details, though her academic contributions remain centered on risk assessment and statistical methodologies.

• Statistics
• Nonparametric Regression
• Functional Data Analysis
• +3 more

Wenceslao Gonzalez Manteiga is a Professor at the Department of Statistics, Mathematical Analysis and Optimization within the Faculty of Mathematics at the University of Santiago de Compostela. He leads the MODESTYA research group focusing on optimization, decision, and statistical models with applications in functional data analysis and spatial statistics. Research Interests: Nonparametric regression, functional data analysis, goodness-of-fit tests, missing data, spatiotemporal modeling, and bootstrap techniques. Publications: Over 157 peer-reviewed works since 1985, including influential papers on functional regression, small area estimation, and censored data analysis. Email: wenceslao.gonzalez@usc.es Collaborations: Extensive work with researchers like Ingrid Van Keilegom, Rosa Crujeiras, and Manuel Febrero-Bande, with applications in finance, biostatistics, and geological modeling.

• Statistics
• Optimization
• Biostatistics
• +4 more

Jose Ameijeiras Alonso is a Professor at the University of Santiago de Compostela, affiliated with the Department of Statistics, Mathematical Analysis and Optimization within the Higher Technical School of Engineering. His research focuses on statistical methodology, optimization, and applications in health, environmental, and spatial data analysis. He holds a PhD in Statistics from the University of Santiago de Compostela (2017), specializing in density estimation and mode assessment. Key research interests include nonparametric estimation techniques, circular data analysis, and epidemiological modeling. Notable contributions include the development of the Multimode R package for mode detection and the SPQEIR model for assessing epidemic suppression strategies. His work bridges theoretical statistics with practical applications in wildfire analysis, viral disease studies, and bioinformatics. Recent publications (2021–2024) emphasize circular statistics, multimodality tests, and clinical applications in ME/CFS and multiple sclerosis. He collaborates with institutions like the Galician Mathematical Research and Technology Center (CITMAga) and leads the MODESTYA research group, focusing on decision models and statistical applications.

• Statistics
• Operational Research
• Nonparametric Regression
• +2 more

César Andrés Sánchez Sellero is a Professor in the Department of Statistics, Mathematical Analysis and Optimization at the Faculty of Mathematics, University of Santiago de Compostela. He is affiliated with the Galician Mathematical Research and Technology Center (CITMAga) and leads research in the MODESTYA group (Optimization, Decision, Statistical Models and Applications). His academic career spans over 25 years, with a focus on statistical inference for censored/truncated data, nonparametric regression, and quantile modeling. PhD in Statistics (2001) from University of Santiago de Compostela Research Interests: Specializes in advanced statistical methodologies for complex data structures, including censored and truncated datasets. Key contributions in goodness-of-fit testing, bandwidth selection algorithms, and empirical likelihood frameworks for regression models. Publication Trends: His work bridges theoretical statistics with practical applications in data analysis, particularly in nonparametric methods and quantile regression. Collaborations with researchers like Wenceslao González-Manteiga and Valentin Patilea have produced significant results in error distribution estimation and model diagnostics.

• Statistics
• Operational Research
• Probability Theory
• +3 more

María Ángeles Saavedra González is a researcher at the Department of Statistics and Operational Research, University of Vigo, Spain. She earned her doctorate from the University of Oviedo in 1998 with a thesis on non-parametric density estimation for moving average processes under the supervision of Dr. Ricardo Cao Abad. University: University of Vigo Department: Statistics and Operational Research Education: PhD (1998) from University of Oviedo Her research spans Statistics, Operational Research, and Probability Theory, focusing on nonparametric estimation, functional data analysis, and stochastic processes. Recent work includes environmental applications like air quality outlier detection and oil spill modeling. Her publications (10 since 1999) include collaborations in computational statistics, energy optimization for atomic clusters, and environmental simulations. Notable co-authors include Ricardo Cao, Susana Gómez, and David Romero. She has contributed to zbMATH with 10 publications, covering topics in time series analysis, global optimization algorithms, and environmental remediation strategies.

• Computational Statistics
• Nonparametric Regression
• Survival Analysis
• +4 more

Nora M. Villanueva is an Assistant Professor at the Department of Statistics and Operations Research, University of Vigo. She works at the intersection of computational statistics, nonparametric regression, and survival analysis, with applications to cybersecurity and industry 4.0. Her academic journey includes a PhD in Statistics and Operations Research from the University of Vigo (2021) and prior roles as a Data Scientist at Optare Solutions and Ecomanagement Technology. Education: PhD in Statistics and Operations Research (2021), University of Vigo Her research focuses on predictive modeling, nonparametric curve estimation, and software development for statistical analysis. She has contributed to the creation of four R packages and participated in 17 research projects and 39 conferences. Her most recent publications explore group determination in survival curves, explainable AI methods, and genomic sequence analysis. Her work combines theoretical statistics with practical applications in telecommunications, energy, and biomedical domains. Scientific Awards: Research Award (Science and Technology), Pontevedra Provincial Council (Spain) Premio a la mejor solución del reto IN1: 'Clasificación de ataques a honeypots SSH' Villanueva has extensive teaching experience at the University of Vigo, covering courses in statistics, regression models, and data analytics. She also contributed to industry workshops on predictive modeling and R package development.