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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2020 - 20223
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Author: Balakrishnan, Narayanaswamy × Has files: true ×

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Now showing 1 - 3 of 3
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    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.
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    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.
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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.