Maria Dolores Ugarte

Professor

Universidad de Navarra

Spain

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Maria Dolores Ugarte is a Professor at Universidad Publica de Navarra, Spain. Her research interests include spatio-temporal analysis, disease mapping, and adaptive learning approaches in statistical modeling. Recent articles authored by her explore topics such as cancer incidence prediction, multivariate time forecasting in industrial processes, and spatial confounding mitigation in areal models.

Articles (17)

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2025

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2022

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2022

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2021

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2021

Collaborators (5)

Mehdi Moradi

Associate professor

Umea University

SWEDEN

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Guzman Santafe

Lecturer

Universidad de Navarra

SPAIN

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Paula Camelia Trandafir

Universidad Pública de Navarra (UPNA)

SPAIN

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Ana F Militino

Professor

Public University of Navarre

SPAIN

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Ana María Insausti Serrano

Profesor Titular de Universidad

Universidad de Navarra

SPAIN

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Maria Dolores Ugarte

Research Interests

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About

Articles (17)

On prior smoothing with discrete spatial data in the context of disease mapping

Acciones y reflexiones desde la Presidencia de FENStatS

An Adaptive Learning Approach to Multivariate Time Forecasting in Industrial Processes

A fast approach for analyzing spatio-temporal patterns in ischemic heart disease mortality across US counties (1999–2021)

A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models

Unpaired spatio-temporal fusion of image patches (USTFIP) from cloud covered images

Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women

Predicting cancer incidence in regions without population-based cancer registries using mortality

Evaluating recent methods to overcome spatial confounding

Using mortality to predict incidence for rare and lethal cancers in very small areas

Body composition and resting energy expenditure in a group of children with achondroplasia: Effectiveness of predictive models in the treatment of obesity

A scalable approach for short‐term disease forecasting in high spatial resolution areal data

Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach

Space-time interactions in Bayesian disease mapping with recent tools: Making things easier for practitioners

Locally adaptive change-point detection (LACPD) with applications to environmental changes

Scalable Bayesian modelling for smoothing disease risks in large spatial data sets using INLA

Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India

Collaborators (5)

Mehdi Moradi

Guzman Santafe

Paula Camelia Trandafir

Ana F Militino

Ana María Insausti Serrano

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