Person:
Martín Apaolaza, Nirian

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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
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2022 - 20233
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Author: Martín Apaolaza, Nirian × Subject: 519.22 ×

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Now showing 1 - 3 of 3
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    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.
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    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.
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    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.