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Urdangarin Iztueta, Arantxa

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https://academica-e.unavarra.es/handle/2454/50107

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Urdangarin Iztueta

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Arantxa

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Estadƭstica, InformƔtica y MatemƔticas

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InaMat2. Instituto de InvestigaciĆ³n en Materiales Avanzados y MatemĆ”ticas

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0000-0002-9101-6459

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811962

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2022 - 20244
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End date: 2024 Ɨ Has files: true Ɨ

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Advances in the estimation of fixed effects in spatial models with random effects
    (2024) Urdangarin Iztueta, Arantxa; Goicoa Mangado, TomƔs; Ugarte Martƭnez, Marƭa Dolores; Estadƭstica, InformƔtica y MatemƔticas; Estatistika, Informatika eta Matematika
    La representaciĆ³n cartogrĆ”fica de enfermedades permite estimar indicadores de salud especĆ­ficos para Ć”reas geogrĆ”ficas dentro de una regiĆ³n de estudio. Aunque el objetivo principal suele ser proporcionar las tasas/riesgos de incidencia o mortalidad de enfermedades como el cĆ”ncer, existen otras aplicaciones. Por ejemplo, el anĆ”lisis de crĆ­menes contra las mujeres en India. La mayor parte de la investigaciĆ³n en la representaciĆ³n cartogrĆ”fica de enfermedades usa modelos mixtos de Poisson jerĆ”rquicos bayesianos que incorporan la dependencia espacial o temporal para suavizar los riesgos y reducir la variabilidad de los estimadores clĆ”sicos de los riesgos como las razones de incidencia/mortalidad estandarizadas (RIE/RME). Sin embargo, los modelos de representaciĆ³n cartogrĆ”fica de enfermedades tienen algunos inconvenientes. AquĆ­ nos centramos en dos de estas limitaciones. En primer lugar, estos modelos en general no son identificables y se requieren restricciones en el proceso de estimaciĆ³n para obtener resultados razonables. El segundo problema es la confusiĆ³n espacial y estĆ” relacionado con la inclusiĆ³n de covariables en los modelos. Si las covariables tienen estructura espacial, su asociaciĆ³n con la respuesta puede no estimarse bien debido al sesgo y la inflaciĆ³n de la varianza. El objetivo principal de esta tesis es doble. Por un lado, abordaremos la complejidad de incorporar restricciones de suma cero para resolver los problemas de identificaciĆ³n al ajustar modelos espacio-temporales ampliamente utilizados en la representaciĆ³n cartogrĆ”fica de enfermedades utilizando NIMBLE (de Valpine et al., 2017), un sistema para crear modelos estadĆ­sticos en R que permite ajustar modelos jerĆ”rquicos bayesianos utilizando un sistema configurable de algoritmos MCMC. Por otro lado, nos centraremos en la confusiĆ³n espacial, con el objetivo de proponer un mĆ©todo que garantice estimaciones adecuadas de efectos fijos. La presente tesis estĆ” dividida en cuatro capĆ­tulos diferentes. El primer capĆ­tulo proporciona una introducciĆ³n general sobre los problemas que se van a bordar en esta tesis y el resto de los capĆ­tulos profundizan en esos problemas. Esta tesis se cierra con una secciĆ³n final que resume los principales resultados e introduce algunas ideas para futuras investigaciones.
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    PublicationOpen Access
    Evaluating recent methods to overcome spatial confounding
    (Springer, 2022) Urdangarin Iztueta, Arantxa; Goicoa Mangado, TomĆ”s; Ugarte MartĆ­nez, MarĆ­a Dolores; EstadĆ­stica, InformĆ”tica y MatemĆ”ticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad PĆŗblica 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 1995ā€“2001 and lip cancer incidence data in Scotland between the years 1975ā€“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, TomƔs; Kneib, Thomas; Ugarte Martƭnez, Marƭa Dolores; Estadƭstica, InformƔtica y MatemƔticas; 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.
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    PublicationOpen Access
    Space-time interactions in bayesian disease mapping with recent tools: making things easier for practitioners
    (Edward Arnold, 2022) Urdangarin Iztueta, Arantxa; Ugarte Martƭnez, Marƭa Dolores; Goicoa Mangado, TomƔs; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadƭstica, InformƔtica y MatemƔticas
    Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time.