Person: Ugarte Martínez, María Dolores
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Ugarte Martínez
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María Dolores
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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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0000-0002-3505-8400
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387
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Publication Open Access Estimación del desempleo por comarcas en Navarra(Gobierno de Navarra, Departamento de Economía y Hacienda, 2005) Ugarte Martínez, María Dolores; Militino, Ana F.; González Ramajo, Begoña; Goicoa Mangado, Tomás; Sagaseta López, M.; Estadística e Investigación Operativa; Estatistika eta Ikerketa OperatiboaEl conocimiento del desempleo en una región es un indicador potente del ritmo de crecimiento de una economía, ya que de forma indirecta mide su capacidad para generar empleo. El Instituto de Estadística de Navarra está apostando por proporcionar en un futuro cercano estimaciones del desempleo a un nivel cada vez más desagregado. La heterogeneidad de las comarcas navarras y el interés mostrado por administraciones locales y sindicatos, hace necesario tener un conocimiento de la situación de desempleo a nivel comarcal, evitando así descansar únicamente en el resultado global para toda Navarra tal y como lo proporciona la Encuesta de Población Activa (EPA). La tarea es compleja, pero está incardinada además en uno de los objetivos prioritarios del proyecto europeo EURAREA, del cual ha formado parte el Instituto Nacional de Estadística (INE), y por ende, el Instituto de Estadística de Navarra. Es decir, hay un interés real en Europa por proporcionar estimaciones a nivel comarcal. En Navarra esta tarea ya ha comenzado y en este congreso presentamos algunos de los resultados obtenidos. En particular se ilustran las estimaciones preliminares derivadas de la aplicación de diversos estimadores basados en el diseño para obtener la proporción de parados por sexo en las siete comarcas de Navarra. Se compara además el comportamiento de diversos estimadores en términos del sesgo relativo y del error cuadrático medio relativo. Los estimadores ofrecidos permiten calcular además la estimación del número de ocupados e inactivos, así como de sus correspondientes tasas.Publication Open Access Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India(SAGE Publications, 2021) Adin Urtasun, Aritz; Goicoa Mangado, Tomás; Hodges, James S.; Schnell, Patrick M.; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y MatemáticasAssessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects, while obtaining good model predictions. In particular, restricted regression and an apparently—though in fact not—equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India.Publication Open Access Big problems in spatio-temporal disease mapping: methods and software(Elsevier, 2023) Orozco Acosta, Erick; Adin Urtasun, Aritz; 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, PJUPNA20001Background and objective: Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal areal data, with special emphasis on mortality/incidence relative risk estimation. Methods: We present a pragmatic and simple idea that permits hierarchical spatio-temporal models to be fitted when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming or even unfeasible. Results: Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users. Conclusions: Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.Publication Open Access Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences(Springer, 2024) Echegoyen Arruti, Carlos; Pérez, Aritz; Santafé Rodrigo, Guzmán; Pérez Goya, Unai; 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 PublikoaTemporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images. Our approach relies on a combination of deep embeddings and time series clustering to capture the semantic properties of the ground and its evolution over time, providing a comprehensive understanding of the region of interest. The proposed method is enhanced by a novel procedure specifically devised to refine the embedding and exploit the underlying spatio-temporal patterns. We use this methodology to conduct an in-depth analysis of a 220 km region in northern Spain in different settings. The results provide a broad and intuitive perspective of the land where large areas are connected in a compact and well-structured manner, mainly based on climatic, phytological, and hydrological factors.Publication Open 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 PublikoaThe 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 inflatedPublication Open Access Logistic regression versus XGBoost for detecting burned areas using satellite images(Springer, 2024) Militino, Ana F.; Goyena Baroja, Harkaitz; Pérez Goya, Unai; 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 PublikoaClassical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant problem: the identification of burned areas through satellite imagery, that is a clear example of imbalanced data. The methods are illustrated in the North-Central Portugal and the North-West of Spain in October 2017 within a multi-temporal setting of satellite imagery. Daily satellite images are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Our analysis shows that a classical Logistic regression (LR) model competes on par, if not surpasses, a widely employed machine learning algorithm called the extreme gradient boosting algorithm (XGBoost) within this particular domain.Publication Open Access Using RGISTools to estimate water levels in reservoirs and lakes(MDPI, 2020) Militino, Ana F.; Montesino San Martín, Manuel; Pérez Goya, Unai; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y MatemáticasThe combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images.Publication Open Access Hybrid pine (Pinus attenuata × Pinus radiata) somatic embryogenesis: what do you prefer, mother or nurse?(MDPI, 2021) Montalbán, Itziar A.; Castander Olarieta, Ander; Hargreaves, Cathy L.; Gough, Keiko; Reeves, Cathie B.; Ballekom, Shaf van; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Moncaleán, Paloma; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaDevelopment of hybrid pines of Pinus radiata D. Don for commercial forestry presents an opportunity to diversify the current resource of plant material. Climate change and different land uses pose challenges, making alternative species necessary to guarantee wood and non-wood products in the future. Pinus radiata var. cedrosensis × Pinus attenuata hybrid possesses different attributes, such as tolerance to drought conditions, better growth and resistance to snow damage at higher altitudes, and more importantly, different wood quality characteristics. Embryogenic cell lines were successfully initiated reciprocal hybrids using as initial explants megagametophytes, excised zygotic embryos and excised zygotic embryos plus nurse culture. However, the questions raised were: does the initiation environment affect the conversion to somatic plantlets months later? Does the mother tree or the cross have an effect on the conversion to somatic plantlets? In the present work we analysed the maturation rate, number of somatic embryos, germination rate, and the ex-vitro growth in cell lines derived from different initiation treatments, mother tree species, and crosses. Differences were not observed for in vitro parameters such as maturation and germination. However, significant differences were observed due to the mother tree species in relation with the ex-vitro growth rates observed, being higher those in which P. radiata acted as a mother. Moreover, embryogenic cell lines from these hybrids were stored at −80◦C and regenerated after one and five years.Publication Open Access Interpolation of the mean anomalies for cloud filling in land surface temperature and normalized difference vegetation index(IEEE, 2019) Militino, Ana F.; Ugarte Martínez, María Dolores; Pérez Goya, Unai; Genton, Marc G.; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y MatemáticasWhen monitoring time series of remote sensing data, it is advisable to fill gaps, i.e., missing or distorted data, caused by atmospheric effects or technical failures. In this paper, a new method for filling these gaps called interpolation of the mean anomalies (IMA) is proposed and compared with some competitors. The method consists of: 1) defining a neighborhood for the target image from previous and subsequent images across previous and subsequent years; 2) computing the mean target image of the neighborhood; 3) estimating the anomalies in the target image by subtracting the mean image from the target image; 4) filtering the anomalies; 5) averaging the anomalies over a predefined window; 6) interpolating the averaged anomalies; and 7) adding the interpolated anomalies to the mean image. To assess the performance of the IMA method, both a real example and a simulation study are conducted with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and MODIS AQUA images captured over the region of Navarre (Spain) from 2011 to 2013. We analyze the land surface temperature (LST) day and night, and the normalized difference vegetation index (NDVI). In the simulation study, seven sizes of artificial clouds are randomly introduced to each image in the studied time series. The square root of the mean-squared prediction error (RMSE) between the original and the filled data is chosen as an indicator of the goodness of fit. The results show that the IMA method outperforms Timesat, Hants, and Gapfill (GF) in filling small, moderate, and big cloud gaps in both the day and night LST and NDVI data, reaching RMSE reductions of up to 23%.Publication Open Access Steering the synthesis of Fe3O4 nanoparticles under sonication by using a fractional factorial design(Elsevier, 2021) Echeverría Morrás, Jesús; Moriones Jiménez, Paula; Garrido Segovia, Julián José; Ugarte Martínez, María Dolores; Cervera Gabalda, Laura María; Garayo Urabayen, Eneko; Gómez Polo, Cristina; Pérez de Landazábal Berganzo, José Ignacio; Ciencias; Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa; Gobierno de Navarra / Nafarroako GobernuaSuperparamagnetic iron oxide nanoparticles (MNPs) have the potential to act as heat sources in magnetic hyperthermia. The key parameter for this application is the specific absorption rate (SAR), which must be as large as possible in order to optimize the hyperthermia treatment. We applied a Plackett-Burman fractional factorial design to investigate the effect of total iron concentration, ammonia concentration, reaction temperature, sonication time and percentage of ethanol in the aqueous media on the properties of iron oxide MNPs. Characterization techniques included total iron content, Fourier Transform Infrared Spectroscopy, X-Ray Diffraction, High Resolution Transmission Electron Microscopy, and Dynamic Magnetization. The reaction pathway in the coprecipitation reaction depended on the initial Fe concentration. Samples synthesized from 0.220 mol L−1 Fe yielded magnetite and metastable precipitates of iron oxyhydroxides. An initial solution made up of 0.110 mol L−1 total Fe and either 0.90 or 1.20 mol L−1 NH3(aq) led to the formation of magnetite nanoparticles. Sonication of the reaction media promoted a phase transformation of metastable oxyhydroxides to crystalline magnetite, the development of crystallinity, and the increase of specific absorption rate under dynamic magnetization.