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
3 results
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Item Minimum phi-divergence estimator and hierarchical testing in loglinear models(Statistica Sinica, 2000) Cressie, Noel A.; Pardo Llorente, LeandroIn this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum phi-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a phi-divergence goodness-of-fit statistic, which is the basis of two new statistics for solving the problem of testing a nested sequence of loglinear models.Item Model checking in loglinear models using phi-divergences and MLEs(Journal of statistical planning and inference, 2002) Cressie, Noel A.; Pardo Llorente, LeandroConsider the loglinear model for categorical data under the assumption of either Poisson, multinomial, or product-multinomial sampling. We are interested in testing between various hypotheses on the parameter space. In this paper, the usual likelihood ratio test, with maximum likelihood estimators for the unspecified parameters, is generalized to tests based on phi-divergences, still using maximum likelihood estimators. These tests yield the likelihood ratio test as a special case. Asymptotic distributions for the new test statistics are derived under both the null and the alternative hypotheses. Then it is shown how the phi-divergences can be used to test nested hypotheses, yielding a type of "analysis of divergence".Item Size and power considerations for testing loglinear models using phi-divergence test statistics(Statistica Sinica, 2003) Cressie, Noel A.; Pardo Llorente, Leandro; Pardo Llorente, María del CarmenIn this article, we assume that categorical data axe distributed according to a multinomial distribution whose probabilities follow a loglinear model. The inference problem we consider is that of hypothesis testing in a loglinear-model setting. The null hypothesis is a composite hypothesis nested within the alternative. Test statistics are chosen from the general class of phi-divergence statistics. This article collects together the operating characteristics of the hypothesis test based on both asymptotic (using large-sample theory) and finite-sample (using a designed simulation study) results. Members of the class of power divergence statistics are compared, and it is found that the Cressie-Read statistic offers an attractive alternative to the Pearson-based and the likelihood ratio-based test statistics, in terms of both exact and asymptotic size and power.