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Goicoa Mangado, Tomás

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https://academica-e.unavarra.es/handle/2454/49150

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Goicoa Mangado

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Tomás

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Estadística, Informática y Matemáticas

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InaMat2. Instituto de Investigación en Materiales Avanzados y Matemáticas

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0000-0002-0588-0137

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88598

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2591

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Now showing 1 - 10 of 32
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    PublicationOpen Access
    Online relative risks/rates estimation in spatial and spatio-temporal disease mapping
    (Elsevier, 2019) Adin Urtasun, Aritz; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    Background and objective: Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed. Methods: The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference. Results: The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided. Conclusions: Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.
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    PublicationOpen Access
    La necesidad de un manifiesto a favor de la alfabetización estadística: editorial
    (Sociedad Estadística e Investigación Operativa (SEIO), 2025-03-27) Casals, Martí ; Daunis i Estadella, Pepus; Galé, Carmen; Goicoa Mangado, Tomás; Patino-Alonso, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
    Imagina leer el siguiente titular en un periódico: “Las personas que consumen café tienen un 50% menos de probabilidad de desarrollar ciertas enfermedades”. A partir de esta lectura, decides aumentar tu consumo diario de café. Sin embargo, antes de tomar esa decisión hay que conocer cómo se ha llegado a dicha conclusión: cuál es el tamaño de la muestra, el diseño del estudio, sobre qué población se aplica, el método de recolección de datos, y valorar si es posible establecer relaciones causales. Este ejemplo ilustra como la falta de conocimiento sobre el procedimiento puede comprometer la validez del estudio y se puede traducir en una interpretación incorrecta o unos resultados sesgados. Una comunicación deficiente puede llevar decisiones mal fundamentadas, incluso en áreas críticas como la salud y el bienestar (Spiegelhalter, 2019).
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    PublicationOpen Access
    Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas
    (Springer, 2020) Vicente Fuenzalida, Gonzalo; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001-2014.
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    PublicationOpen Access
    Comments on: Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions
    (Springer, 2019) Goicoa Mangado, Tomás; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    This paper comments the article 'Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions', where the authors address the important topic of building very general models with interaction terms facing the relevant issue of identifiability.
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    PublicationOpen Access
    A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters
    (SAGE, 2018-04-13) Adin Urtasun, Aritz; Lee, Duncan; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
    Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.
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    PublicationOpen Access
    Pinus spp. somatic embryo conversion under high temperature: effect on the morphological and physiological characteristics of plantlets
    (MDPI, 2020) Marqués do Nascimento, Antonia Maiara; Barroso, Priscila Alves; Ferreira do Nascimento, Naysa Flavia; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Montalbán, Itziar A.; Moncaleán, Paloma; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    Climatic variations in the current environmental scenario require plants with tolerance to sudden changes in temperature and a decrease in water availability. Accordingly, this tolerance will enable successful plantations and the maintenance of natural and planted forests. Consequently, in the last two decades, drought tolerance and high temperatures in conifers have been an important target for morphological, physiological, and epigenetic studies. Based on this, our research team has optimized different stages of somatic embryogenesis (SE) in Pinus spp. improving the success of the process. Through this method, we can obtain a large amount of clonal material and then analyze the somatic plants under different conditions ex vitro. The analysis of the morphological and physiological parameters in somatic embryos (ses) and plants with different tolerances to abiotic stress can provide us with valuable information about the mechanisms used by plants to survive under adverse environmental conditions. Thus, the objective of this work was to evaluate the influence of high temperatures (23, 40, 50, and 60◦C, after 12 weeks, 90, 30, 5 min, respectively) on the morphology of somatic embryos obtained from Pinus radiata D.Don (Radiata pine) and Pinus halepensis Mill. (Aleppo pine). In addition, we carried out a physiological evaluation of the somatic plants of P. radiata submitted to heat and water stress in a greenhouse. We observed that the number of somatic embryos was not affected by maturation temperatures in both species. Likewise, P. radiata plants obtained from these somatic embryos survived drought and heat stress in the greenhouse. In addition, plants originating from embryonal masses (EMs) subjected to high maturation temperature (40 and 60◦C) had a significant increase in gs and E. Therefore, it is possible to modulate the characteristics of somatic plants produced by the manipulation of environmental conditions during the process of SE.
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    PublicationOpen Access
    Small area estimation of gender-based violence: rape incidence risks in Uttar Pradesh, India
    (Society of Statistics, Computer and Applications, 2018) Vicente Fuenzalida, Gonzalo; Goicoa Mangado, Tomás; Puranik, A; Ugarte Martínez, María Dolores; Estatistika eta Ikerketa Operatiboa; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística e Investigación Operativa
    Violence against women is considered an endemic problem in communities and countries around the world, and it has been declared an issue of epidemic proportions by the World Health Organization (WHO). In India, where the patriarchal nature of the country contributes to increasing violence against women, there has been a dramatic increase of this gender-based violence in the past decades. In this paper we focus on analyzing rape incidence risks in the most populous state of India. In particular, small area models including spatial, temporal, and spatio-temporal components are used to estimate rape incidence risks in the districts of Uttar Pradesh during the period 2001-2014. We discover interesting spatio-temporal patterns of rape incidence as well as point out districts with significant high risks.
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    Búsqueda de submercados inmobiliarios mediante modelos de mixturas
    (Gobierno de Navarra, Departamento de Economía y Hacienda, 2003) Militino, Ana F.; Ugarte Martínez, María Dolores; Goicoa Mangado, Tomás; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
    La heterogeneidad presente en el mercado inmobiliario dificulta enormemente su análisis y puede conllevar la presencia de submercados. En este caso, el modelo clásico de regresión lineal múltiple, ampliamente utilizado con este tipo de datos, puede no ser adecuado y, por tanto, es necesaria la utilización de técnicas estadísticas más específicas que resuelvan el problema de la heterogeneidad y de la búsqueda de submercados. En este trabajo se propone un modelo de mixturas de modelos lineales que proporciona un buen ajuste a los datos, a la vez que una clasificación de las observaciones en diferentes grupos o submercados potenciales. El modelo se ilustra mediante el análisis de un conjunto de 293 viviendas usadas de Pamplona.
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    PublicationOpen Access
    Evaluating recent methods to overcome spatial confounding
    (Springer, 2022) Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995–2001 and lip cancer incidence data in Scotland between the years 1975–1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value although standard errors could be inflated
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    PublicationOpen Access
    A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models
    (Elsevier, 2024) Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Kneib, Thomas; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
    Spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a simplified version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyse two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses.