Person:
Martín Apaolaza, Nirian

First Name

Nirian

Last Name

Martín Apaolaza

URI

https://hdl.handle.net/20.500.14352/76377

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Comercio y Turismo

Department

Economía Financiera, Actuarial y Estadística

Area

Estadística e Investigación Operativa

Identifiers

Full item page

Search Results

Now showing 1 - 9 of 9

Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
(Entropy, 2020) Castilla González, Elena María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro; Zografos, Konstantinos
This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α. After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion.
Robust semiparametric inference for polytomous logistic regression with complex survey design
(Advances in Data Analysis and Classification, 2020) Castilla González, Elena María; Ghosh, Abhik; Martín Apaolaza, Nirian; Pardo Llorente, Leandro
Analyzing polytomous response from a complex survey scheme, like stratified or cluster sampling is very crucial in several socio-economics applications. We present a class of minimum quasi weighted density power divergence estimators for the polytomous logistic regression model with such a complex survey. This family of semiparametric estimators is a robust generalization of the maximum quasi weighted likelihood estimator exploiting the advantages of the popular density power divergence measure. Accordingly robust estimators for the design effects are also derived. Using the new estimators, robust testing of general linear hypotheses on the regression coefficients are proposed. Their asymptotic distributions and robustness properties are theoretically studied and also empirically validated through a numerical example and an extensive Monte Carlo study
Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators
(Statistics and Computing, 2022) Castilla González, Elena María; Jaenada Malagón, María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro
Since the two seminal papers by Fisher (1915, 1921) were published, the test under a fixed value correlation coefficient null hypothesis for the bivariate normal distribution constitutes an important statistical problem. In the framework of asymptotic robust statistics, it remains being a topic of great interest to be investigated. For this and other tests, focused on paired correlated normal random samples, Rényi's pseudodistance estimators are proposed, their asymptotic distribution is established and an iterative algorithm is provided for their computation. From them the Wald-type test statistics are constructed for different problems of interest and their influence function is theoretically studied. For testing null correlation in different contexts, an extensive simulation study and two real data based examples support the robust properties of our proposal.
Robust Inference for One-Shot Device Testing Data Under Weibull Lifetime Model
(IEEE Transactions on Reliability, 2020) Balakrishnan, Narayanaswamy; Castilla González, Elena María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro
Classical inferential methods for one-shot device testing data from an accelerated life-test are based on maximum likelihood estimators (MLEs) of model parameters. However, the lack of robustness of MLE is well-known. In this article, we develop robust estimators for one-shot device testing by assuming a Weibull distribution as a lifetime model. Wald-type tests based on these estimators are also developed. Their robustness properties are evaluated both theoretically and empirically, through an extensive simulation study. Finally, the methods of inference proposed are applied to three numerical examples. Results obtained from both Monte Carlo simulations and numerical studies show the proposed estimators to be a robust alternative to MLEs.
Project number: 166
Tutorial interactivo de ejemplos básicos y ejercicios de inferencia estadística no-paramétrica mediante software libre: implementación mediante R, R-studio y Swirl
(2019) Martín Apaolaza, Nirian; Castilla González, Elena María; Miranda Menéndez, Pedro; Pardo Llorente, Leandro
Project number: 343
Tutoriales guiados de prácticas en “Estadística: Análisis de Datos e Inferencia” mediante el software libre SAS University Edition
(2020) Martín Apaolaza, Nirian; Castilla González, Elena María; Chocano Feito, Pedro José; Jaenada Malagón, María; Pardo Llorente, Leandro
Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
(Quality and Reliability Engineering International, 2020) Balakrishnan, Narayanaswamy; Castilla González, Elena María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro
Introduced robust density-based estimators in the context of one-shot devices with exponential lifetimes under a single stress factor. However, it is usual to have several stress factors in industrial experiments involving one-shot devices. In this paper, the weighted minimum density power divergence estimators (WMDPDEs) are developed as a natural extension of the classical maximum likelihood estimators (MLEs) for one-shot device testing data under exponential lifetime model with multiple stresses. Based on these estimators, Wald-type test statistics are also developed. Through a simulation study, it is shown that some WMDPDEs have a better performance than the MLE in relation to robustness. Two examples with multiple stresses show the usefulness of the model and, in particular, of the proposed estimators, both in engineering and medicine.
Power divergence approach for one-shot device testing under competing risks
(Journal of Computational and Applied Mathematics, 2022) Balakrishnan, Narayanaswamy; Castilla González, Elena María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro
Most work on one-shot devices assume that there is only one possible cause of device failure. However, in practice, it is often the case that the products under study can experience any one of various possible causes of failure. Robust estimators and Wald-type tests are developed here for the case of one-shot devices under competing risks. An extensive simulation study illustrates the robustness of these divergence-based estimators and test procedures based on them. A data-driven procedure is proposed for choosing the optimal estimator for any given data set which is then applied to an example in the context of survival analysis.
Project number: 118
Docencia de optimización en el entorno virtual Moodle a partir de ejercicios resueltos
(2022) Miranda Menéndez, Pedro; Castilla González, Elena María; Chocano Feito, Pedro José; Jaenada Malagón, María; Martín Apaolaza, Nirian; Martínez Suárez, Susana; Pardo Llorente, Leandro
El presente proyecto trata de mejorar las herramientas que tienen a su disposición los alumnos en el aprendizaje de la asignatura de Optimización. Uno de los problemas a los que se enfrentan los alumnos de esta asignatura es la escasez de bibliografía explícita referente a estos temas. Al ser ya una asignatura muy específica de los estudios de Matemáticas, no hay libros generales que traten estos temas. Por ello, los alumnos están obligados a utilizar un grupo muy reducido de libros de texto. Además, aunque en estos libros se tratan los aspectos teóricos de la asignatura, no contienen una selección de problemas resueltos que permitan a los alumnos evaluar su dominio sobre los algoritmos que aparecen en esta asignatura. Por otra parte, el entorno exam de R se ha desarrollado mucho en los últimos años. En la versión anterior, se podían generar ejercicios de forma aleatoria y a continuación producir un fichero .pdf para su impresión, pensando todavía en un uso en papel. Con la nueva versión del paquete exam todo lo anterior sigue siendo posible pero además permite que estos programas se puedan adecuar a las herramientas online, permitiendo su inclusión como una herramienta en los entornos de enseñanza virtual como Moodle. El objetivo principal de este proyecto es cubrir estas necesidades, de forma que los alumnos cuenten con una herramienta que les permita ejercitarse y profundizar en los aspectos metodológicos de la asignatura a partir de problemas resueltos.