Person: Pardo Llorente, Leandro
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First Name
Leandro
Last Name
Pardo Llorente
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Department
Estadística e Investigación Operativa
Area
Estadística e Investigación Operativa
Identifiers
5 results
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Now showing 1 - 5 of 5
Item Influence analysis of robust Wald-type tests(Journal Of Multivariate Analysis, 2016) Ghosh, A.; Mandal, A.; Martin, N.; Pardo Llorente, LeandroWe consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum likelihood estimators. An extensive study of their robustness properties is given though the influence functions as well as the chi-square inflation factors. It is theoretically established that the level and power of these robust tests are stable against outliers, whereas the classical Wald test breaks down. Some numerical examples confirm the validity of the theoretical results.Item Wald type and phi-divergence based test-statistics for isotonic binomial proportions.(Mathematics and computers in simulation, 2016) Martin, N.; Mata, R.; Pardo Llorente, LeandroIn this paper new test statistics are introduced and studied for the important problem of testing hypothesis that involves inequality constraint on proportions when the sample comes from independent binomial random variables: Wald type and phi-divergence based test-statistics. As a particular case of phi-divergence based test-statistics, the classical likelihood ratio test is considered. An illustrative example is given and the performance of all of them for small and moderate sample sizes is analyzed in an extensive simulation study. (C) 2015 International Association for Mathematics and Computers in SimulationItem ϕ-Divergence Based Procedure for Parametric Change-Point Problems(Methodology and computing in applied probability, 2016) Batsidis, Apostolos; Martin, N.; Pardo Llorente, Leandro; Zografos, K.This paper studies the change-point problem for a general parametric, univariate or multivariate family of distributions. An information theoretic procedure is developed which is based on general divergence measures for testing the hypothesis of the existence of a change. For comparing the exact sizes of the new test-statistic using the criterion proposed in Dale (J R Stat Soc B 48–59, 1986), a simulation study is performed for the special case of exponentially distributed random variables. A complete study of powers of the test-statistics and their corresponding relative local efficiencies, is also considered.Item Empirical phi-divergence test statistics for the difference of means of two populations(Asta- Advances in Statistical Analysis, 2017) Balakrishnan, N.; Martin, N.; Pardo Llorente, LeandroEmpirical phi-divergence test statistics have demostrated to be a useful technique for the simple null hypothesis to improve the finite sample behavior of the classical likelihood ratio test statistic, as well as for model misspecification problems, in both cases for the one population problem. This paper introduces this methodology for two-sample problems. A simulation study illustrates situations in which the new test statistics become a competitive tool with respect to the classical z test and the likelihood ratio test statistic.Item Robust tests for the equality of two normal means based on the density power divergence(Metrika, 2015) Basu, A.; Mandal, A.; Martin, N.; Pardo Llorente, LeandroStatistical techniques are used in all branches of science to determine the feasibility of quantitative hypotheses. One of the most basic applications of statistical techniques in comparative analysis is the test of equality of two population means, generally performed under the assumption of normality. In medical studies, for example, we often need to compare the effects of two different drugs, treatments or preconditions on the resulting outcome. The most commonly used test in this connection is the two sample test for the equality of means, performed under the assumption of equality of variances. It is a very useful tool, which is widely used by practitioners of all disciplines and has many optimality properties under the model. However, the test has one major drawback; it is highly sensitive to deviations from the ideal conditions, and may perform miserably under model misspecification and the presence of outliers. In this paper we present a robust test for the two sample hypothesis based on the density power divergence measure (Basu et al. in Biometrika 85(3):549-559, 1998), and show that it can be a great alternative to the ordinary two sample test. The asymptotic properties of the proposed tests are rigorously established in the paper, and their performances are explored through simulations and real data analysis.