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

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Search Results

Now showing 1 - 10 of 19

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.
New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation
(Journal of Computational and Applied Mathematics, 2020) Alonso Revenga, Juana María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro; Brugnano, Luigi; Efendiev, Yalchin; Keller, André; Kwok-Po, Michael; Romani, Lucia; Tank. Fatih
Traditionally, the Dirichlet-multinomial distribution has been recognized as a key model for contingency tables generated by cluster sampling schemes. There are, however, other possible distributions appropriate for these contingency tables. This paper introduces new statistics capable of testing log-linear modeling hypotheses with distributional unspecification, when the individuals of the clusters are possibly homogeneously correlated. An estimator for the intracluster correlation coefficient, valid for different cluster sizes, plays a crucial role in the construction of the goodness-of-fit test-statistics.
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
Project number: 245
Big data en educación II: metodologías adaptativas en el proceso de enseñanza-aprendizaje desde el diagnóstico del estudiante
(2019) 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, Mª Desamparados; Álvarez Sáez, Manuel; Ferrer Caja, José María; Aparicio Sánchez, María Del Socorro; Barreal Pernas, Jesús; Jannes, Gil
Phi-Divergence test statistics applied to latent class models for binary data
(Trends in Mathematical, Information and Data Sciences, Trends in Mathematical, Information and Data Sciences, 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.
Robustness of Minimum Density Power Divergence Estimators and Wald-type test statistics in loglinear models with multinomial sampling
(Journal of computational and applied mathematics, 2021) Calviño Martínez, Aída; Martín Apaolaza, Nirian; Pardo Llorente, Leandro; Brugnano, Luigi; Efendiev, Yalchin; Keller, André
In this paper we propose a new family of estimators, Minimum Density Power Divergence Estimators (MDPDE), as a robust generalization of maximum likelihood estimators (MLE) for the loglinear model with multinomial sampling by using the Density Power Divergence (DPD) measure introduced by Basu et al. (1998). Based on these estimators, we further develop two types of confidence intervals (asymptotic and bootstrap ones), as well as a new robust family of Wald-type test statistics for testing a nested sequence of loglinear models. Furthermore, we study theoretically the robust properties of both the MDPDE as well as Wald-type tests through the classical influence function analysis. Finally, a simulation study provides further confirmation of the validity of the theoretical results established in the paper.