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
19 results
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Now showing 1 - 10 of 19
Item Nonadditivity in loglinear models using phi-divergences and MLEs(Journal of Statistical Planning and Inference, 2005) Pardo Llorente, Leandro; Pardo Llorente, María del CarmenIn this paper three families of test statistics for testing nonadditivity in loglinear models are presented under the assumption of either Poisson, multinomial, or product-multinomial sampling. These new families are based on the phi-divergence measures. The standard method for testing nonadditivity is used, i.e., the two-stage tests procedure. In this procedure the parameters are first estimated using an additive model and then the estimates are treated as known constants for the second stage of the procedure. These test statistics, which are asymptotically chi-squared, generalize the likelihood ratio test for this problem given by Christensen and Utts (J. Statist. Plann. Inference 33 (1992) 333). An example and a simulation study are included.Item Preliminary phi-divergence test estimators for linear restrictions in a logistic regression model(Statistical Papers, 2009) Menéndez Calleja, María Luisa; Pardo Llorente, Leandro; Pardo Llorente, María del CarmenThe problem of estimation of the parameters in a logistic regression model is considered under multicollinearity situation when it is suspected that the parameter of the logistic regression model may be restricted to a subspace. We study the properties of the preliminary test based on the minimum phi-divergence estimator as well as in the phi-divergence test statistic. The minimum phi-divergence estimator is a natural extension of the maximum likelihood estimator and the phi-divergence test statistics is a family of the test statistics for testing the hypothesis that the regression coefficients may be restricted to a subspace.Item Minimum phi-divergence estimator in logistic regression models(Statistical Papers, 2006) Pardo Llorente, Julio Ángel; Pardo Llorente, Leandro; Pardo Llorente, María del CarmenA general class of minimum distance estimators for logistic regression models based on the phi- divergence measures is introduced: The minimum phi- divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. Its asymptotic properties are studied as well as its behaviour in small samples through a simulation study.Item Extension of the Wald statistic to models with dependent observations(Metrika, 2000) Morales González, Domingo; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen; Vadja, IgorA generalization of the Wald statistic for testing composite hypotheses is suggested for dependent data from exponential models which include Levy processes and diffusion fields. The generalized statistic is proved to be asymptotically chi-squared distributed under regular composite hypotheses. It is simpler and more easily available than the generalized likelihood ratio statistic. Simulations in an example where the latter statistic is available show that the generalized Wald test achieves higher average power than the generalized likelihood ratio test.Item Minimum power-divergence estimator in three-way contingency tables(Journal of Statistical Computation and Simulation, 2003) Pardo Llorente, Leandro; Pardo Llorente, María del CarmenCressie et al. (2000; 2003) introduced and studied a new family of statistics, based on the phi-divergence measure, for solving the problem of testing a nested sequence of loglinear models. In that family of test statistics the parameters are estimated using the minimum phi-divergence estimator which is a generalization of the maximum likelihood estimator. In this paper we study the minimum power-divergence estimator (the most important family of minimum phi-divergence estimator) for a nested sequence of loglinear models in three-way contingency tables under assumptions of multinomial sampling. A simulation study illustrates that the minimum chi-squared estimator is simultaneously the most robust and efficient estimator among the family of the minimum power-divergence estimator.Item An exploratory canonical analysis approach for multinomial populations based on the phi-divergence measure(Kybernetika, 2004) Pardo Llorente, Julio Ángel; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen; Zografos, KonstantinosIn this paper we consider an exploratory canonical analysis approach for multinomial population based on the phi-divergence measure. We define the restricted minimum phi-divergence estimator, which is seen to be a generalization of the restricted maximum likelihood estimator. This estimator is then used in phi-divergence goodness-of-fit statistics which is the basis of two new families of statistics for solving the problem of selecting the number of significant correlations as well as the appropriateness of the model.Item An extension of likelihood-ratio-test for testing linear hypotheses in the baseline-category logit model(Computational statistics and data analysis, 2008) Pardo Llorente, Leandro; Pardo Llorente, María del CarmenA new family of test statistics for testing linear hypotheses in baseline-category logit models is introduced and its asymptotic distribution is obtained. The new family is a natural extension of the classical likelihood ratio test. A simulation study is carried out to find new test statistics that offer an attractive alternative to the classical likelihood ratio test in terms of both exact size and exact power.Item Rényi statistics for testing composite hypotheses in general exponential models.(Statistics, 2004) Morales González, Domingo; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen; Vadja, IgorWe introduce a family of Renyi statistics of orders r is an element of R for testing composite hypotheses in general exponential models, as alternatives to the previously considered generalized likelihood ratio (GLR) statistic and generalized Wald statistic. If appropriately normalized exponential models converge in a specific sense when the sample size (observation window) tends to infinity, and if the hypothesis is regular, then these statistics are shown to be chi(2)-distributed under the hypothesis. The corresponding Renyi tests are shown to be consistent. The exact sizes and powers of asymptotically alpha-size Renyi, GLR and generalized Wald tests are evaluated for a concrete hypothesis about a bivariate Levy process and moderate observation windows. In this concrete situation the exact sizes of the Renyi test of the order r = 2 practically coincide with those of the GLR and generalized Wald tests but the exact powers of the Renyi test are on average somewhat better.Item Preliminary test estimators and phi-divergence measures in generalized linear models with binary data(Journal of multivariate analysis, 2008) Menéndez Calleja, María Luisa; Pardo Llorente, Leandro; Pardo Llorente, María del CarmenWe consider the problem of estimation of the parameters in Generalized Linear Models (GLM) with binary data when it is suspected that the parameter vector obeys some exact linear restrictions which are linearly independent with some degree of uncertainty. Based on minimum phi-divergence estimation (M phi E), we consider some estimators for the parameters of the GLM: Unrestricted M phi E, restricted M phi E, Preliminary M phi E, Shrinkage M phi E, Shrinkage preliminary M phi E, James-Stein M phi E, Positive-part of Stein-Rule M phi E and Modified preliminary M phi E. Asymptotic bias as well as risk with a quadratic loss function are studied under contiguous alternative hypotheses. Some discussion about dominance among the estimators studied is presented. Finally, a simulation study is carried out.Item Julio Angel Pardo Llorente, 1960-2013 (Associate Editor of Statistical Papers) OBITUARY(Statistical Papers, 2013) Pardo Llorente, Leandro; Pardo Llorente, María del Carmen