Person: Susi García, María Del Rosario
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First Name
María Del Rosario
Last Name
Susi García
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
Identifiers
19 results
Search Results
Now showing 1 - 10 of 19
Item Assessing the effect of kurtosis deviations from Gaussianity on conditional distributions(Applied Mathematics and Computation, 2013) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Navarro, H.; Susi García, María Del RosarioThe multivariate exponential power family is considered for n-dimensional random variables, Z, with a known partition Z equivalent to (Y, X) of dimensions p and n - p, respectively, with interest focusing on the conditional distribution Y vertical bar X. An infinitesimal variation of any parameter of the joint distribution produces perturbations in both the conditional and marginal distributions. The aim of the study was to determine the local effect of kurtosis deviations using the Kullback-Leibler divergence measure between probability distributions. The additive decomposition of this measure in terms of the conditional and marginal distributions, Y vertical bar X and X, is used to define a relative sensitivity measure of the conditional distribution family {Y vertical bar X = x}. Finally, simulated results suggest that for large dimensions, the measure is approximately equal to the ratio p/n, and then the effect of non-normality with respect to kurtosis depends only on the relative size of the variables considered in the partition of the random vector.Item Calculando la matriz de covarianzas con la estructura de una red Bayesiana Gaussiana(2012) Gómez Villegas, Miguel Ángel; Susi García, María Del RosarioEn este trabajo se introduce una fórmula recursiva que permite calcular la matriz de covarianzas de una red Bayesiana Gaussiana dados los parámetros de la especificación condicionada de la parte cuantitativa del modelo. Además se determinan las varianzas y las covarianzas del problema considerando los distintos caminos que aparecen en el grafo que recoge la parte cualitativa de la red.- Project number: 155
Item Desarrollo de herramientas on line para estudiar la inserción laboral de los graduados de la Universidad Complutense(2015) Susi García, María Del Rosario; Brita-Paja, Jose Luis; Nieto Zayas, Carmen; Amador Pacheco, Julia; Cáceres García, Inés María Item Características del parto y resultados neonatales en mujeres inmigrantes(Metas de enfermería, 2016) Maroto Alonso, Virginia; Patiño Maraver, Vicente Manuel; Susi García, María Del Rosario; Álvarez Plaza, ConsueloObjetivo: identificar las características específicas del parto y resultados neonatales según la nacionalidad de las mujeres en el Hospital Infanta Cristina de Parla (Madrid). Método: estudio observacional longitudinal con recogida retrospectiva de información, utilizando datos de 404 partos (202 de mujeres gestantes españolas y 202 de mujeres gestantes inmigrantes) asistidos en el Servicio de Ginecología y Obstetricia del Hospital Infanta Cristina de la localidad de Parla (Madrid) durante los años 2012 y 2013. Resultados: la población inmigrante proviene de hasta 30 países diferentes. Las mujeres gestantes africanas son el grupo mayoritario (37%), seguidas por americanas (28%), resto de Europa (19%) y Asia (16%). Las mujeres gestantes inmigrantes del área de salud estudiada tienen menor edad (media 29 años (DE: 5,1)), menor duración de la gestación (media 39,2 semanas DE: 1,2), menor índice de utilización de analgesia epidural (71,8%) y neonatos con mayor peso (peso medio 3.352 g DE: 431) que las mujeres gestantes españolas, de manera estadísticamente significativa. Los resultados obstétricos y perinatales referentes al tipo de registro cardiotocográfico, tipo de parto, pinzamiento de cordón umbilical, test de Apgar, pH de arteria umbilical y tipo de reanimación neonatal no presentan diferencias significativas entre inmigrantes y españolas. Conclusiones: las mujeres gestantes inmigrantes del área estudiada presentan características obstétricas similares a las españolas. A diferencia de las españolas son más jóvenes, utilizan menos la analgesia epidural y sus recién nacidos presentan mayor peso al nacimiento.Item Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis(PLoS ONE, 2018) González Pérez, Mariano; Susi García, María Del Rosario; Barrio De Santos, Ana Rosa; Antona Peñalba, BeatrizPurpose: To quantify the levels of performance (symptom severity) of the computer-vision symptom scale (CVSS17), confirm its bifactorial structure as detected in an exploratory factor analysis, and validate its factors as subscales. Methods: By partial credit model (PCM), we estimated CVSS17 measures and the standard error for every possible raw score, and used these data to determine the number of different performance levels in the CVSS17. In addition, through discriminant analysis, we checked that the scale's two main factors could classify subjects according to these determined levels of performance. Finally, a separate Rasch analysis was performed for each CVSS17 factor to assess their measurement properties when used as isolated scales. Results: We identified 5.8 different levels of performance. Discriminant functions obtained from sample data indicated that the scale's main factors correctly classified 98.4% of the cases. The main factors: Internal symptom factor (ISF) and external symptom factor (ESF) showed good measurement properties and can be considered as subscales. Conclusion: CVSS17 scores defined five different levels of performance. In addition, two main factors (ESF and ISF) were identified and these confirmed by discriminant analysis. These subscales served to assess either the visual or the ocular symptoms attributable to computer use.Item Extreme Inaccuracies In Gaussian Bayesian Networks(Journal Of Multivariate Analysis, 2008) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Susi García, María Del RosarioTo evaluate the impact of model inaccuracies over the network’s output, after the evidence propagation, in a Gaussian Bayesian network, a sensitivity measure is introduced. This sensitivity measure is the Kullback–Leibler divergence and yields different expressions depending on the type of parameter to be perturbed, i.e. on the inaccurate parameter. In this work, the behavior of this sensitivity measure is studied when model inaccuracies are extreme,i.e. when extreme perturbations of the parameters can exist. Moreover, the sensitivity measure is evaluated for extreme situations of dependence between the main variables of the network and its behavior with extreme inaccuracies. This analysis is performed to find the effect of extreme uncertainty about the initial parameters of the model in a Gaussian Bayesian network and about extreme values of evidence. These ideas and procedures are illustrated with an example.Item Malnutrición asociada a disfagia orofaríngea en pacientes mayores de 65 años ingresados en una unidad médico-quirúrgica(Enfermería Clínica, 2014) Susi García, María Del Rosario; Martínez Rincón, María Del Carmen; Morena López, Felipe de la; Cortázar Sáez, Milagros; Santander Vaquero, Cecilio; Galán Sánchez-Heredero, María JoséObjetivos: El objetivo principal es conocer la relación existente entre disfagia orofaríngea, situación de riesgo nutricional y deterioro funcional en pacientes mayores de 65 años ingresados en una unidad de hospitalización médico-quirúrgica. Los objetivos secundarios son determinar la prevalencia de disfagia orofaríngea, conocer el estado nutricional y la capacidad funcional de estos pacientes. Metodología: Estudio observacional, analítico y transversal que incluyó durante los meses de febrero a marzo del 2013 a pacientes mayores de 65 años ingresados en la unidad de Digestivo-Urología del Hospital Universitario de la Princesa. Se registraron las siguientes variables: edad, sexo, índice de masa corporal, soporte familiar, diagnóstico, comorbilidad, disfagia orofaríngea (EAT-10 y método de evaluación clínica volumen-viscosidad), malnutrición (Mininutritional Assessment) y capacidad funcional de los pacientes (índice de Barthel). Resultados: Se reclutó a 167 pacientes, siendo la prevalencia de disfagia y de malnutrición del 30,8 y 15,4% respectivamente. En pacientes con disfagia orofaríngea la prevalencia de problemas nutricionales aumentó hasta el 75%. En el análisis de regresión logística, la obtención de una puntuación baja en el índice de Barthel (OR 0,97 [IC 95%; 0,95-0,99]), la presencia de comorbilidad (OR 7,98 [IC 95%; 3,09-20,61]) y padecer disfagia (OR 4,07 [IC 95%; 1,57-10,52]) se asociaron a una mayor probabilidad de padecer malnutrición. Discusión: La disfagia orofaríngea es uno de los problemas más infradiagnosticados y subestimados entre los pacientes ancianos y que mayor afectación tiene sobre su estado nutricional. Sugerimos detectarla de forma precoz mediante los métodos diagnósticos establecidos y con la colaboración de un equipo multidisciplinar.Item Sensitivity to hyperprior parameters in Gaussian Bayesian networks(2010) Gómez Villegas, Miguel Á.; Main Yaque, Paloma; Navarro, H.; Susi García, María Del RosarioOur focus is on learning Gaussian Bayesian networks (GBNs) from data. In GBNs the multivariate normal joint distribution can be alternatively specified by the normal regression models of each variable given its parents in the DAG (directed acyclic graph). In the later representation the paramenters are the mean vector, the regression coefficients and the corresponding conditional variances. the problem of Bayesian learning in this context has been handled with different approximations, all of them concerning the use of different priors for the parameters considered we work with the most usual prior given by the normal/inverse gamma form. In this setting we are inteserested in evaluating the effect of prior hyperparameters choice on posterior distribution. The Kullback-Leibler divergence measure is used as a tool to define local sensitivity comparing the prior and posterior deviations. This method can be useful to decide the values to be chosen for the hyperparameters.Item Sensitivity to hyperprior parameters in Gaussian Bayesian networks(Journal of multivariate analysis, 2014) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Navarro, H.; Susi García, María Del RosarioBayesian networks (BNs) have become an essential tool for reasoning under uncertainty in complex models. In particular, the subclass of Gaussian Bayesian networks (GBNs) can be used to model continuous variables with Gaussian distributions. Here we focus on the task of learning GBNs from data. Factorization of the multivariate Gaussian joint density according to a directed acyclic graph (DAG) provides an alternative and interchangeable representation of a GBN by using the Gaussian conditional univariate densities of each variable given its parents in the DAG. With this latter conditional specification of a GBN, the learning process involves determination of the mean vector, regression coefficients and conditional variances parameters. Some approaches have been proposed to learn these parameters from a Bayesian perspective using different priors, and therefore some hyperparameter values are tuned. Our goal is to deal with the usual prior distributions given by the normal/inverse gamma form and to evaluate the effect of prior hyperparameter choice on the posterior distribution. As usual in Bayesian robustness, a large class of priors expressed by many hyperparameter values should lead to a small collection of posteriors. From this perspective and using Kullback-Leibler divergence to measure prior and posterior deviations, a local sensitivity measure is proposed to make comparisons. If a robust Bayesian analysis is developed by studying the sensitivity of Bayesian answers to uncertain inputs, this method will also be useful for selecting robust hyperparameter values.Item Sensitivity Analysis in Gaussian Bayesian Networks Using a Divergence Measure(Communications in statistics. Theory and methods, 2007) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Susi García, María Del RosarioThis article develops a method for computing the sensitivity analysis in a Gaussian Bayesian network. The measure presented is based on the Kullback–Leibler divergence and is useful to evaluate the impact of prior changes over the posterior marginal density of the target variable in the network. We find that some changes do not disturb the posterior marginal density of interest. Finally, we describe a method to compare different sensitivity measures obtained depending on where the inaccuracy was. An example is used to illustrate the concepts and methods presented.