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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2017 - 20203
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Author: Ghosh, Abhik × Has files: true ×

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Now showing 1 - 3 of 3
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    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
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    A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator
    (Electronic Journal of Statistics, 2017) Basu, Ayanendranath; Ghosh, Abhik; Mandal, Abhijit; Martín Apaolaza, Nirian; Pardo Llorente, Leandro
    In this paper a robust version of the classical Wald test statistics for linear hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. A family of robust Wald type tests are considered here, where the minimum density power divergence estimator is used instead of the maximum likelihood estimator. We obtain the asymptotic distribution and also study the robustness properties of these Wald type test statistics. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.
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    A New Class of Robust Two-Sample Wald-Type Tests
    (The International Journal of Biostatistics, 2018) Ghosh, Abhik; Martín Apaolaza, Nirian; Basu, Ayanendranath; Pardo Llorente, Leandro
    Parametric hypothesis testing associated with two independent samples arises frequently in several applications in biology, medical sciences, epidemiology, reliability and many more. In this paper, we propose robust Wald-type tests for testing such two sample problems using the minimum density power divergence estimators of the underlying parameters. In particular, we consider the simple two-sample hypothesis concerning the full parametric homogeneity as well as the general two-sample (composite) hypotheses involving some nuisance parameters. The asymptotic and theoretical robustness properties of the proposed Wald-type tests have been developed for both the simple and general composite hypotheses. Some particular cases of testing against one-sided alternatives are discussed with specific attention to testing the effectiveness of a treatment in clinical trials. Performances of the proposed tests have also been illustrated numerically through appropriate real data examples.