Person:
Main Yaque, Paloma

First Name

Paloma

Last Name

Main Yaque

URI

https://hdl.handle.net/20.500.14352/82088

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Ciencias Matemáticas

Area

Estadística e Investigación Operativa

Identifiers

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Now showing 1 - 10 of 21

On tail behavior in Bayesian location inference
(Statistics and probability letters, 1997) Main Yaque, Paloma; Navarro Veguillas, Hilario
The asymptotic behavior in the right tail of the hazard rate function is considered to compare probability distributions. Using this tail ordering, the position of the posterior distribution with respect to the prior and the likelihood distributions is analyzed for a Bayesian location problem, and it is proved that, under rather general conditions, the posterior distribution is equivalent to the lightest-tailed distribution, except when both the likelihood and the prior are very heavy-tailed distributions. The relationship between the posterior distributions based on random samples of sizes n and 1, respectively, is also studied, as well as its dependence on the relative position of the prior distribution and the model for observations in the hazard rate scale.
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 Rosario
The 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.
A suitable Bayesian approach in testing point null hypothesis: some examples revisited
(Communications in statistics. Theory and methods, 2002) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Sanz San Miguel, Luis
In the problem of testing the point null hypothesis H-0: theta = theta(0) versus H-1: theta not equal theta(0), with a previously given prior density for the parameter theta, we propose the following methodology: to fix an interval of radius epsilon around theta(0) and assign a prior mass, pi(0), to H-0 computed by the density pi(theta) over the interval (theta(0) - epsilon, theta(0) + epsilon), spreading the remainder, 1 - pi(0), over H-1 according to pi(theta). It is shown that for Lindley's paradox, the Normal model with some different priors and Darwin-Fisher's example, this procedure makes the posterior probability of H-0 and the p-value matching better than if the prior mass assigned to H-0 is 0.5.
Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications
(Cancer research, 2015) Fresno Vara, Juan Angel; Main Yaque, Paloma; y, otros
Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER+) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER+ and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention.
Asymptotic behaviour of reliability functions
(Statistics & Probability Letters, 1988) Main Yaque, Paloma
We relate the classes of life distributions based on notions of aging with those that depend on the tail behaviour. This last classification introduces the concepts of outlier-neutral, outlier-prone and outlier- resistant distributions. We prove some results that connect IFR, IFRA, NBU and NBUE definitions with the outlier-resistance concept. Similarly the relationship of DFR, DFRA, NWU and NWUE to outlier-proneness is obtained.
Clasificación de distribuciones y datos atípicos
(2015) Main Yaque, Paloma; Gómez Villegas, Ángel
Simulación de Sucesos Discretos. Prácticas en casos reales con R. Competición de estudiantes de simulación
(2016) Main Yaque, Paloma
La Simulación de Sucesos Discretos (SSD)es una metodología que permite aplicar los procedimientos de simulación estocástica, para representar un sistema en el que las variables aleatorias que lo componen están relacionadas entre si. En esta monografía se recogen distintos casos reales en los que puede aplicarse SSD junto con su implementación en R y resultados finales.
Functional proteomics outlines the complexity of breast cancer molecular subtypes
(Scientific reports, 2017) Gamez Pozo, A.; Trilla Fuentes, L.; Berges Soria, J.; Selevsek, N.; López Vacas, R.; Díaz Almiron, M.; Nanni,, P.; Arevalillo, J. M.; Navarro, H.; Grossmann, J.; Moreno, F. G.; Rioja, R. G.; Prado Vazquez, G.; Zapater Moros, A.; Main Yaque, Paloma; Feliu, J.; Del Prado, P.; Zamora, P.; Ciruelos Gil, Eva María; Espinosa, E.; Vara, J. A.F.
Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptorpositive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expressionbased probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.
A Bayesian Analysis For The Multivariate Point Null Testing Problem
(Statistics, 2009) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Sanz San Miguel, Luis
A Bayesian test for the point null testing problem in the multivariate case is developed. A procedure to get the mixed distribution using the prior density is suggested. For comparisons between the Bayesian and classical approaches, lower bounds on posterior probabilities of the null hypothesis, over some reasonable classes of prior distributions, are computed and compared with the p-value of the classical test. With our procedure, a better approximation is obtained because the p-value is in the range of the Bayesian measures of evidence.
Extreme Inaccuracies In Gaussian Bayesian Networks
(Journal Of Multivariate Analysis, 2008) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Susi García, María Del Rosario
To 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.