Person:
Martín Apaolaza, Nirian

First Name

Nirian

Last Name

Martín Apaolaza

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Comercio y Turismo

Department

Economía Financiera, Actuarial y Estadística

Area

Estadística e Investigación Operativa

Identifiers

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Now showing 1 - 10 of 10

Robust inference for one‐shot device testing data under exponential lifetime model with multiple stresses
(Wiley, 2020-05-20) 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.
Robust semiparametric inference for polytomous logistic regression with complex survey design
(Springer, 2020-11-23) 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
Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
(https://www.mdpi.com/, 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 approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators
(Springer Nature, 2022-10-25) 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.
Power divergence approach for one-shot device testing under competing risks
(Elsevier, 2022-08-27) 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.
Phi-Divergence test statistics applied to latent class models for binary data
(Springer, 2023) Miranda Menéndez, Pedro; Felipe Ortega, Ángel; Martín Apaolaza, Nirian
In this paper we present two new families of test statistics for studying the problem of goodness-of-fit of some data to a latent class model for dichotomous questions based on phi-divergence measures. We also treat the problem of selecting the best model out of a sequence of nested latent class models. In both problems, we study the asymptotic distribution of the corresponding test statistics, showing that they share the same behavior as the corresponding maximum likelihood test statistic.
Big data en educación II: metodologías adaptativas en el proceso de enseñanza-aprendizaje desde el diagnóstico del estudiante
(2019-01-22) Hernández Estrada, Adolfo; García Pérez, Enrique; Fernández-Cid Enríquez, Matilde; Vela Pérez, María; Peñaloza Figueroa, Juan Luis; Martínez Rodríguez, María Elena; Arteaga Martínez, Blanca; Macías Sánchez, Jesús; Martín Apaolaza, Nirian; Fernández-Crehuet Santos, José María; Pérez Martín, María; Mateos-Aparicio Morales, Gregoria; Fernández Molina, María Elia; Dorado Sánchez, Juan; Ruozzi López, Alberto; Martíns Pinto, Ana Rita; Martínez de La Fuente, Jorge Iván; Andrés García, Ángel de; Carrasco Pradas, M. Desamparados; Álvarez Sáez, Manuel; Ferrer Caja, José María; Aparicio Sánchez, María del Socorro; Barreal Pernas, Jesús; Jannes, Gil
Tutoriales guiados de prácticas en “Estadística: Análisis de Datos e Inferencia” mediante el software libre SAS University Edition
(2020-05-24) Martín Apaolaza, Nirian; Castilla González, Elena María; Chocano Feito, Pedro José; Jaenada Malagón, María; Pardo Llorente, Leandro
Tutorial interactivo de ejemplos básicos y ejercicios de inferencia estadística no-paramétrica mediante software libre: implementación mediante R, R-studio y Swirl
(2019-07-04) Martín Apaolaza, Nirian; Castilla González, Elena María; Miranda Menéndez, Pedro; Pardo Llorente, Leandro
Big data en educación: tipologías de los estudiantes a partir del estudio de las interacciones dentro del triángulo pedagógico
(2017-09-29) Hernández Estrada, Adolfo; Martínez Rodríguez, María Elena; Casado de Lucas, David; Peñaloza Figueroa, Juán Luis; Pérez Martín, María; Arteaga Martínez, Blanca; Martín Apaolaza, Nirian; Macías Sánchez, Jesús; Fernández Molina, María Elia; Ruozzi López, Alberto; Martins Pinto, Ana Rita; Martínez de la Fuente, Jorge Ivan; Fernández-Crehuet Santos, José María; Vela Pérez, María; Dorado Sánchez, Juán Francisco