Person:
Castilla González, Elena María

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First Name
Elena María
Last Name
Castilla González
URI
https://hdl.handle.net/20.500.14352/75718
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Department
Estadística e Investigación Operativa
Area
Estadística e Investigación Operativa
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2017 - 20202
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Now showing 1 - 2 of 2
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    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.
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    Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator
    (Entropy, 2017) Castilla González, Elena María; Martín, Nirian; Pardo Llorente, Leandro; Zografos, Konstantinos
    In this paper, a robust version of the Wald test statistic for composite likelihood is considered by using the composite minimum density power divergence estimator instead of the composite maximum likelihood estimator. This new family of test statistics will be called Wald-type test statistics. The problem of testing a simple and a composite null hypothesis is considered, and the robustness is studied on the basis of a simulation study. The composite minimum density power divergence estimator is also introduced, and its asymptotic properties are studied.