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  • Estimación bayesiana de cópulas extremales en procesos de Poisson

     Ortego Martinez, Maria Isabel
    Universitat Politècnica de Catalunya
    Theses

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    La estimación de probabilidades de ocurrencia de cantidades extremales es imprescindible en el estudio de la peligrosidad de fenómenos naturales. Las cantidades extremales de interés suelen corresponder a fenómenos caracterizados por dos o más magnitudes, que en muchos casos son dependientes entre sí. Por tanto, para poder caracterizar mejor las situaciones que pudieran resultar peligrosas, se deben estudiar conjuntamente las magnitudes que describen el fenómeno. Se ha establecido un modelo Poisson-GPD que permite describir la ocurrencia de los sucesos extremales y sus tamaños marginales: la ocurrencia de los sucesos extremales se representa mediante un proceso de Poisson y cada suceso se caracteriza por un tamaño modelado según una distribución generalizada de Pareto, GPD. La dependencia entre sucesos se modeliza mediante funciones cópula: se utiliza una familia de cópulas Gumbel, adecuada al tipo de datos, y se introduce un nuevo tipo de cópula, la cópula CrEnC. La cópula CrEnC minimiza la información mutua en situaciones donde se dispone de información parcial en forma de restricciones, tales como los modelos marginales o momentos conjuntos de las variables. La representación de estas cópulas en R^2 permite mejorar tanto su estima como la apreciación de la bondad de ajuste a los datos. Se proporciona un algoritmo de estimación de cópulas CrEnC, que incluye una aproximación de las funciones normalizadoras mediante el método Montecarlo.En este contexto los datos suelen ser escasos, por lo que la incertidumbre en la estimación del modelo será elevada. Se ha establecido un proceso de estimación bayesiana de los parámetros, la cual permite tener en cuenta esta incertidumbre. La bondad de ajuste de diversos aspectos del modelo (bondad de ajuste GPD, hipótesis GPD-Weibull y bondad de ajuste global) se ha valorado mediante una selección de p-valores bayesianos, los cuales incorporan la incertidumbre de la estimación de los parámetros. Una vez estimado el modelo, se realiza un post-proceso de la información, donde se obtienen cantidades a posteriori de interés, como probabilidades de excedencia de valores de referencia o periodos de retorno de sucesos de un tamaño determinado.El modelo propuesto se aplica a tres conjuntos de datos de características diferentes. Se obtienen buenos resultados: las cópulas CrEnC introducidas representan correctamente la dependencia en situaciones en las que sólo se dispone de información parcial y la estimación bayesiana de los parámetros del modelo proporciona valor añadido a los resultados, ya que permite evaluar la incertidumbre de las estimaciones y tenerla en cuenta al obtener las cantidades a posteriori deseadas.

  • Bayesian trend analysis of extreme wind using observed and hindcast series off Catalan coast, NW Mediterranean Sea

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José; Tolosana Delgado, Raimon
    Natural hazards and earth system sciences
    Vol. 2, p. 799-824
    DOI: 10.5194/nhessd-2-799-2014
    Date of publication: 2014-01-29
    Journal article

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    It has been suggested that climate change might modify the occurrence rate and magnitude of large ocean-wave and wind storms. The hypothesised reason is the increase of available energy in the atmosphere-ocean system. Forecasting models are commonly used to assess these effects, given that good quality data series are often too short. However, forecasting systems are often tuned to reproduce the average behavior, and there are concerns on their relevance for extremal regimes. We present a methodology of simultaneous analysis of observed and hindcasted data with the aim of extracting potential time drifts as well as systematic regime discrepancies between the two data sources. The method is based on the Peak-Over-Threshold (POT) approach and the Generalized Pareto Distribution (GPD) within a Bayesian estimation framework. In this context, storm events are considered points in time, and modelled as a Poisson process. Storm magnitude over a reference threshold is modelled with a GPD, a flexible model that captures the tail behaviour of the magnitude distribution. All model parameters, i.e. shape and location of the magnitude GPD and the Poisson occurrence rate, are affected by a trend in time. Moreover, a systematic difference between parameters of hindcasted and observed series is considered. Finally, the posterior joint distribution of all these trend parameters is studied using a conventional Gibbs sampler. This method is applied to compare hindcast and observed series of 10 min average wind speed at a deep buoy location off the Catalan coast (NE Spain, Western Mediterranean; buoy data from 2001; REMO wind hindcasting from 1958 on). Appropriate scale and domain of attraction are discussed, and the reliability of trends in time are addressed.

  • Anàlisis de Dades Composicionals i Espacials. Compositional and Spatial Data Analysis (COSDA)

     Martín Fernández, Josep Antoni; Ortego Martinez, Maria Isabel
    Competitive project

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  • Bayesian trend analysis of extreme wind using observed and hindcast series off the Catalan coast, NW Mediterranean Sea

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José; Tolosana Delgado, Raimon
    Natural hazards and earth system sciences
    Vol. 14, num. 9, p. 2387-2397
    DOI: 10.5194/nhess-14-2387-2014
    Date of publication: 2014-01-01
    Journal article

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    It has been suggested that climate change might modify the occurrence rate and magnitude of large ocean-wave and wind storms. The hypothesised reason is the increase of available energy in the atmosphere-ocean system. Forecasting models are commonly used to assess these effects, given that good-quality data series are often too short. However, forecasting systems are often tuned to reproduce the average behaviour, and there are concerns on their relevance for extremal regimes. We present a methodology of simultaneous analysis of observed and hindcast data with the aim of extracting potential time drifts as well as systematic regime discrepancies between the two data sources. The method is based on the peak-over-threshold (POT) approach and the generalized Pareto distribution (GPD) within a Bayesian estimation framework. In this context, storm events are considered points in time, and modelled as a Poisson process. Storm magnitude over a reference threshold is modelled with a GPD, a flexible model that captures the tail behaviour of the magnitude distribution.; All model parameters, i.e. shape and location of the magnitude GPD and the Poisson occurrence rate, are affected by a trend in time. Moreover, a systematic difference between parameters of hindcast and observed series is considered. Finally, the posterior joint distribution of all these trend parameters is studied using a conventional Gibbs sampler. This method is applied to compare hindcast and observed series of average wind speed at a deep buoy location off the Catalan coast (NE Spain, western Mediterranean; buoy data from 2001; REMO wind hindcasting from 1958 on). Appropriate scale and domain of attraction are discussed, and the reliability of trends in time is addressed.

  • Modeling extremal dependence using copulas. Application to rainfall data

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José; Tolosana Delgado, Raimon
    Conference of the International Association for Mathematical Geosciences
    p. 53-56
    DOI: 10.1007/978-3-642-32408-6
    Presentation's date: 2013-09-02
    Presentation of work at congresses

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    Spurious copulas  Open access

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    International Workshop on Compositional Data Analysis
    p. 123-130
    Presentation's date: 2013-06-05
    Presentation of work at congresses

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    Modeling dependence between two or more variables is a common issue in statistical applications. The Pearson correlation coefficient is often used to measure dependence, although it

  • Métodos Estadísticos en Espacios Restringidos (Metrics)

     Ortego Martinez, Maria Isabel; Martín Fernández, Josep Antoni
    Competitive project

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  • Bayes spaces: use of improper distributions and exponential families

     Egozcue Rubi, Juan José; Pawlowsky-Glahn, Vera; Tolosana Delgado, Raimon; Ortego Martinez, Maria Isabel; van den Boogaart, K. Gerald
    Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A, Matemáticas
    Vol. 107, num. 2, p. 475-486
    DOI: 10.1007/s13398-012-0082-6
    Date of publication: 2013
    Journal article

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    Bayes spaces are vector spaces of sigma-additive positive measures. Proportional measures are considered equivalent and can be represented by densities with respect to a fixed dominating measure. The addition in these spaces is perturbation. It corresponds to Bayes theorem, which appears as a linear operation. Bayes spaces, with continuous dominating measures, contain finite and infinite measures. Finite measures are equivalent to probability measures. Infinite measures include what in Bayesian statistics are called improper priors and non-integrable likelihood functions, justifying the use of such improper densities in Bayes theorem. Many concepts of probability theory can be handled in a natural way in the context of Bayes spaces. Particularly, an exponential family of probability densities appears as a cone contained in an affine subspace of the Bayes space. The framework of Bayes spaces allows an easy handling of exponential families and their extensions to improper distributions. Furthermore, the vector space structure of Bayes spaces allows the definition of derivatives of densities. In Bayesian statistics, these derivatives are a new tool to examine sensitivity of posterior distributions with respect to both observed data and prior changes.

  • Bayesian trend analysis of extreme wind hazard using observed and hindcast series off Catalan coast, NE Mediterranean Sea

     Egozcue Rubi, Juan José; Ortego Martinez, Maria Isabel; Cunillera i Grañó, Jordi; Tolosana Delgado, Raimon
    EGU Plinius Conference on Mediterranean Storms
    Presentation's date: 2012-11-13
    Presentation of work at congresses

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  • Assessing wavestorm hazard evolution in the NW Mediterranean with hindcast and buoy data

     Ortego Martinez, Maria Isabel; Tolosana Delgado, Raimon; Gibergans Báguena, José; Egozcue Rubi, Juan José; Sanchez-arcilla Conejo, Agustin
    Climatic change
    Vol. 113, num. 3-4, p. 713-731
    DOI: 10.1007/s10584-011-0388-y
    Date of publication: 2012-07-15
    Journal article

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  • Bayes spaces: use of improper priors and distances between densities

     Egozcue Rubi, Juan José; Pawlowsky-Glahn, Vera; Tolosana Delgado, Raimon; Ortego Martinez, Maria Isabel; van den Boogaart, K. Gerald
    Workshop Métodos Bayesianos
    Presentation's date: 2011-11-11
    Presentation of work at congresses

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    Checking model-data weather hazard occurrence fit in the context of climate change  Open access

     Tolosana Delgado, Raimon; Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José; Sanchez-arcilla Conejo, Agustin
    Conference of the International Association for Mathematical Geosciences
    p. 870-877
    DOI: 10.5242/iamg.2011.0101
    Presentation's date: 2011-09-06
    Presentation of work at congresses

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    In climate change impact studies it is common to run a given response model (from ecosystem changes to wavestorm or landslide occurrence) nested into one of the available long-term Global or Regional Circulation Models (GCM, RCM) reproducing the climate for the XX century or predicting it for the XXI. In this way, it is expected to capture the average behaviour of the studied system to a changing climate forcing: in other words, with such response forecasts, one does not actually expect to be able to reproduce each and every single event, but rather its statistical behaviour. Regarding weather-related hazard, the relevant statistical properties are the occurrence return period of events, and their expected magnitude. The present study focuses on wave storm occurrence, and aims at presenting a general methodology to check the adequate reproduction of the return period of hazardous weather-related events by such response forecast models. This is attained by analysing a compound data set formed by series of real data (typically of around 20-30 years in the last decades of the XX century or the beginning of the XXI one) and longer hind- or forecast series. Occurrence of a stormy event is considered to follow an inhomogeneous Poisson process, with: a linear trend to capture climate change, and a step in the junction real data-forecast data to capture systematic model biases. A Bayesian method is proposed to assess the influence of these two elements, i.e the presence/absence of a climate trend and the adequate reproduction of the statistical properties of wavestorm occurrence by forecasting models. Results suggest a non-significant trend albeit negative trend in the storm occurrence, and an inability of the used forecast model to reproduce wavestorm occurrence.

  • Peaks Over Threshold modelling of waveheight buoy and hindcast data to assess storm hazard evolution in the NW Mediterranean

     Ortego Martinez, Maria Isabel; Tolosana Delgado, Raimon; Gibergans Báguena, José; Egozcue Rubi, Juan José; Sanchez-arcilla Conejo, Agustin
    Environmental Risk and Extreme Events
    p. 10
    Presentation's date: 2011-07-11
    Presentation of work at congresses

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    Pluviometric regionalization of Catalunya: a compositional data methodology  Open access

     Gibergans Báguena, José; Ortego Martinez, Maria Isabel; Tolosana Delgado, Raimon
    International Workshop on Compositional Data Analysis
    p. 1-9
    Presentation's date: 2011-05-11
    Presentation of work at congresses

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    The aim of this paper is to introduce a methodology for de¯ning groups from regionalized com- positional data, through a hierarchical clustering algorithm aware of both the spatial dependence and the compositional character of the data set. This method is used to de¯ne a regionalization of Catalunya (NE Spain) with respect to its precipitation patterns in the Winter season. This region is characterized by a highly contrasted topography, which plays a dominant role in the spatial distribution of precipitation. Each rain gauge station is characterized by the relative frequencies of occurrence of six intervals of daily precipitation amount (classes ranging from \no rain" for precipitation below 3 mm, to \heavy storm" above 50 mm). Recognizing that frequencies are com-positional data, the spatial dependence of this data set has been characterized by variograms of the set of all pair-wise log-ratios, in the fashion of the variation matrix. Then, a Mahalanobis distance between stations has been de¯ned using these variograms to ensure that gauges with high spatial correlation get smaller distances. This spatially-dependent distance criterion has been used in a Ward hierarhical cluster method to de¯ne the regions. Results reveal 5 quite homogeneous groups of stations, which can be mostly ascribed a physical meaning. Finally, possible links to regional circulation patterns are discussed.

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    Climate change in a Point-Over-Threshold model: an example on ocean-wave-storm hazard in NE Spain  Open access

     Tolosana Delgado, Raimon; Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José; Sanchez-arcilla Conejo, Agustin
    Advances in geosciences
    Vol. 26, p. 113-117
    DOI: 10.5194/adgeo-26-113-2010
    Date of publication: 2010-09-27
    Journal article

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    A reparametrization of the Generalized Pareto Distribution is here proposed. It is suitable to parsimoniously check trend assumptions within a Point-Over-Threshold model of hazardous events. This is based on considerations about the scale of both the excesses of the event magnitudes and the distribution parameters. The usefulness of this approach is illustrated with a data set from two buoys, where hypotheses about the homogeneity of climate conditions and lack of trends are assessed.

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    Bayesian trend analysis for daily rainfall series of Barcelona  Open access

     Ortego Martinez, Maria Isabel; Gibergans Báguena, José; Tolosana Delgado, Raimon; Egozcue Rubi, Juan José; Llasat Botija, Maria Carmen
    Advances in geosciences
    Vol. 26, p. 71-76
    DOI: 10.5194/adgeo-26-71-2010
    Date of publication: 2010-07-15
    Journal article

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    A Point-Over-Threshold approach using a reparameterization of the Generalized Pareto Distribution (GPD) has been used to assess changes in the daily rainfall Barcelona series (1854–2006). A Bayesian approach, considering the suitable scale and physical features of the phenomenon, has been used to look for these alterations. Two different models have been assessed: existence of abrupt changes in the new GPD parameters due to modifications of the observatory locations and trends in these GPD parameters, pointing to a climate change scenario.

  • Estimating time trends at regional scale from precipitation data

     Ortego Martinez, Maria Isabel; Tolosana Delgado, Raimon; Egozcue Rubi, Juan José
    International Precipitation Conference
    p. 72
    Presentation's date: 2010-06-25
    Presentation of work at congresses

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  • Climate change in a Point-Over-Threshold model: an example on ocean-wave-storm hazard in NE Spain

     Tolosana Delgado, Raimon; Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José; Sanchez-arcilla Conejo, Agustin
    EGU Plinius Conference on Mediterranean Storms
    p. 30
    Presentation's date: 2009-09
    Presentation of work at congresses

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  • Assessing public perceptions on beach quality according to beach users' profile: A case study in the Costa Brava (Spain)

     Roca Bosch, Elisabet; Villares Junyent, Miriam; Ortego Martinez, Maria Isabel
    Tourism management
    Vol. 30, num. 4, p. 598-607
    DOI: 10.1016/j.tourman.2008.10.015
    Date of publication: 2009-08
    Journal article

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  • ANALISIS ESTADISTICO DE DATOS COMPOSICIONALES Y OTROS DATOS CON ESPACIO MUESTRAL RESTRINGIDO

     Graffelman, Jan; Martín Fernández, Josep Antoni; Egozcue Rubi, Juan José; Ortego Martinez, Maria Isabel
    Competitive project

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  • The distribution of extremes in the degree sequence: A Gumbel distribution approach

     Balbuena Martinez, Maria Camino Teofila; Ortego Martinez, Maria Isabel
    Applied mathematics letters
    Vol. 22, num. 4, p. 553-556
    DOI: 10.1016/j.aml.2008.06.028
    Date of publication: 2009-04
    Journal article

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  • P-valores bayesianos uniformes. Aplicación a la bondad de ajuste de la GPD

     Egozcue Rubi, Juan José; Ortego Martinez, Maria Isabel
    Congreso Nacional de Estadística e Investigación Operativa
    p. 46
    Presentation's date: 2009-02-10
    Presentation of work at congresses

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  • Estadística y educación para el desarrollo: una mirada conjunta

     Gibergans Báguena, José; Ortego Martinez, Maria Isabel
    Congreso Nacional de Estadística e Investigación Operativa
    p. 138
    Presentation's date: 2009-02-10
    Presentation of work at congresses

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  • Estadística y educación para el desarrollo: una mirada conjunta

     Gibergans Báguena, José; Ortego Martinez, Maria Isabel
    Congreso Nacional de Estadística e Investigación Operativa
    p. 130
    Presentation's date: 2009-02-10
    Presentation of work at congresses

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    En este trabajo se presentan los materiales "El análisis de datos estadístico en cooperación para el desarrollo: algunos casos prácticos", una herramienta docente que incorpora contenidos estadísticos y aplicaciones multidisciplinares. En el contexto de reforma hacia el EEES es importante una formacion global, con competencias transversales más allá de las técnicas. La ETSECCPB ha impulsado un bloque de asignaturas de Cooperación cuya puesta en marcha hizo patente la necesidad de un enfoque estadístico de los contenidos. Desde entonces se han ido recopilando casos prácticos y experiencias en diferentes ámbitos, conformando un conjunto de materiales utilizados en asignaturas de Cooperación y de Estadística. En las asignaturas donde han sido utilizados han tenido una buena acogida por parte del alumnado y del profesorado. La colaboración con otros miembros de la comunidad universitaria permitirá adaptar y mejorar estos materiales para así ampliar sus contenidos y su ámbito de aplicación.

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    Trend analysis for daily rainfall series of Barcelona  Open access

     Ortego Martinez, Maria Isabel; Gibergans Báguena, José; Tolosana Delgado, Raimon; Egozcue Rubi, Juan José; Llasat Botija, Maria Carmen
    EGU Plinius Conference on Mediterranean Storms
    p. 94
    Presentation of work at congresses

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    Frequency analysis of hydrological series is a key point to acquire an in-depth understanding of the behaviour of hydrologic events. The occurrence of extreme hydrologic events in an area may imply great social and economical impacts. A good understanding of hazardous events improves the planning of human activities.

  • EpD en Ingeniería

     Ortego Martinez, Maria Isabel
    Encuentro de Buenas Prácticas de Educación para el Desarrollo
    Presentation's date: 2008-12-07
    Presentation of work at congresses

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  • Transformaciones de cantidades predictivas. Aplicación a p-valores

     Ortego Martinez, Maria Isabel
    Métodos Bayesianos 2008
    Presentation's date: 2008-11-07
    Presentation of work at congresses

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  • Bootstrap methods in Engineering

     Gibergans Báguena, José; Ortego Martinez, Maria Isabel; Díaz Barrero, José Luis
    Date of publication: 2008-11
    Book chapter

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  • Extremes for scarce data: The role of Bayesian and scaling techniques in reducing uncertainty

     Sanchez-arcilla Conejo, Agustin; Gomez Aguar, Jesus Javier; Egozcue Rubi, Juan José; Ortego Martinez, Maria Isabel; Galiatsatou, P; Prinos, P
    Journal of hydraulic research
    Vol. 46, num. Extra2, p. 224-234
    Date of publication: 2008-08
    Journal article

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  • Bootstrap methods in Engineering education

     Gibergans Báguena, José; Ortego Martinez, Maria Isabel; Díaz Barrero, José Luis
    Education- identity in the process of the integration of Romania in the European Union
    p. 76-85
    Presentation of work at congresses

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  • Métodos Bayesianos08

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    Métodos Bayesianos 2008
    p. 15
    Presentation of work at congresses

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  • Una mirada estadística para la educación al desarrollo

     Ortego Martinez, Maria Isabel; Gibergans Báguena, José
    Congreso Universitario de Innovación Educativa en las Enseñanzas Técnicas
    Presentation of work at congresses

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  • A first approach to self-Learning statistics activities at the UPC

     Ortego Martinez, Maria Isabel; Gibergans Báguena, José
    International journal of mathematical models and methods in applied sciences
    Vol. 1, num. 3, p. 197-200
    Date of publication: 2007-10
    Journal article

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  • The use of Compositional Data to classificate rainfall events: Application to rainfall intensities in Catalonia (Spain)

     Ortego Martinez, Maria Isabel; Gibergans Báguena, José; Egozcue Rubi, Juan José
    European Geosciences Union General Assembly
    p. 9392
    Presentation's date: 2007-04-15
    Presentation of work at congresses

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  • Scale and evaluation of a Poisson-GPD model

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    European Geosciences Union General Assembly
    p. 10031
    Presentation's date: 2007-04-15
    Presentation of work at congresses

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  • Copulas and their extremal transformations

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    22 International Workshop on Statistical Modelling
    p. 463-466
    Presentation of work at congresses

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  • Self-learning statistics activities at the UPC: A first approach

     Gibergans Báguena, José; Ortego Martinez, Maria Isabel
    4th WSEAS/IASME International Conference on Engineering Education
    p. 187-190
    Presentation of work at congresses

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  • The distribution of extremes in the degree sequence: a Gumbel distribution approach

     Balbuena Martinez, Maria Camino Teofila; Ortego Martinez, Maria Isabel
    Encuentro Andaluz de Matemática Discreta
    p. 189-194
    Presentation of work at congresses

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  • Métodos estadísticos sobre el símplex y otros espacios muestrales restringidos (MEASURE)

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    Competitive project

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  • The effect of scale in daily precipitation hazard assessment

     Egozcue Rubi, Juan José; Pawlowsky Glahn, Vera; Ortego Martinez, Maria Isabel; Tolosana Delgado, Raimon
    Natural hazards and earth system sciences
    num. 6, p. 459-470
    Date of publication: 2006-06
    Journal article

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  • Bootstrap: Otra forma de enseñar (y hacer) estadística

     Ortego Martinez, Maria Isabel; Gibergans Báguena, José
    Congreso Universitario de Innovación Educativa en las Enseñanzas Técnicas
    p. 1-10
    Presentation of work at congresses

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  • Escala y evaluación de modelo Poisson-GPD

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    Congreso Nacional de Estadística e Investigación Operativa
    p. 355-356
    Presentation of work at congresses

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  • Densidades de cópulas considerando la estructura de su espacio soporte

     Ortego Martinez, Maria Isabel; Mateu-Figueras, G
    Congreso Nacional de Estadística e Investigación Operativa
    p. 705-706
    Presentation of work at congresses

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  • Wave-height hazard analysis in Eastern Coast of Spain-Bayesian approach using generalized Pareto distribution

     Egozcue Rubi, Juan José; Pawlowsky Glahn, Vera; Ortego Martinez, Maria Isabel
    Advances in geosciences
    num. 2, p. 25-30
    DOI: 10.5194/adgeo-2-25-2005
    Date of publication: 2005-03
    Journal article

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  • Una nueva mirada a la estadistica desde la simulación

     Ortego Martinez, Maria Isabel; Gibergans Báguena, José
    Congreso Universitario de Innovación Educativa en las Enseñanzas Técnicas
    p. 1
    Presentation of work at congresses

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  • Log-scalling rainfall data: effects on GPD Bayesian Goodness of Fit

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    4th Conference on Extreme Value Analysis. Probabilistic and Statistical Models and their Applications.
    p. 1-2
    Presentation of work at congresses

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  • Wave-height hazard analysis in eastern Coast of spain. Bayesian approach using generalized Pareto Distribution

     Egozcue Rubi, Juan José; Pawlowsky Glahn, Vera; Ortego Martinez, Maria Isabel
    6th Plinius Conference on Mediterranean Storms
    p. 1-20
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  • Copulas of Bivariate Excesses and Maxima in Poisson Processes

     Ortego Martinez, Maria Isabel; Egozcue Rubi, Juan José
    3rd International Symposium on Extreme Value Analysis: Theory and Practice
    p. 32
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