Person:
Pardo Llorente, Leandro

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First Name
Leandro
Last Name
Pardo Llorente
URI
https://hdl.handle.net/20.500.14352/75622
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Department
Estadística e Investigación Operativa
Area
Estadística e Investigación Operativa
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Now showing 1 - 10 of 26
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    Rao's statistic for the analysis of uniform association in cross-classifications
    (Communications in statistics. Theory and methods, 2001) Menéndez Calleja, María Luisa; Pardo Llorente, Julio Ángel; Pardo Llorente, Leandro
    In this paper we introduce a new measure for the analysis of association in cross-classifications having ordered categories. Association is measured in terms of the odd-ratios in 2 x 2 subtables formed from adjacent rows and adjacent columns. We focus our attention in the uniform association model. Our measure is based in the family of divergences introduced by Burbea and Rao [1]. Some well-known sets of data are reanalyzed and a simulation study is presented to analyze the behavior of the new families of test statistics introduced in this paper.
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    Divergence-based confidence intervals in false-positive misclassification model
    (Journal of Statistical Computation and Simulation, 2008) Martín Apaolaza, Níriam; Morales González, Domingo; Pardo Llorente, Leandro
    In this article, we introduce minimum divergence estimators of parameters of a binary response model when data are subject to false-positive misclassification and obtained using a double-sampling plan. Under this set up, the problem of goodness-of-fit is considered and divergence-based confidence intervals (CIs) for a population proportion parameter are derived. A simulation experiment is carried out to compare the coverage probabilities of the new CIs. An application to real data is also given.
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    A necessary power divergence-type family of tests for testing elliptical symmetry
    (Journal of Statistical Computation and Simulation, 2014) Batsidis, Apostolos; Martin, Nirian; Pardo Llorente, Leandro; Zografos, Konstantinos
    This paper presents a family of power divergence-type test statistics for testing the hypothesis of elliptical symmetry. We assess the performance of the new family of test statistics, using Monte Carlo simulation. In this context, the type I error rate as well as the power of the tests are studied. Specifically, for selected alternatives, we compare the power of the proposed procedure with that proposed by Schott [Testing for elliptical symmetry in covariance-matrix-based analyses, Stat. Probab. Lett. 60 (2002), pp. 395-404]. This last test statistic is an easily computed one with a tractable null distribution and very good power for various alternatives, as it has established in previous published simulations studies [F. Huffer and C. Park, A test for elliptical symmetry, J. Multivariate Anal. 98 (2007), pp. 256-281; L. Sakhanenko, Testing for ellipsoidal symmetry: A comparison study, Comput. Stat. Data Anal. 53 (2008), pp. 565-581]. Finally, a well-known real data set is used to illustrate the method developed in this paper.
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    Minimum power-divergence estimator in three-way contingency tables
    (Journal of Statistical Computation and Simulation, 2003) Pardo Llorente, Leandro; Pardo Llorente, María del Carmen
    Cressie 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.
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    The generalized entropy measure to the design and comparison of regression experiment in a Bayesian context
    (Information Sciences, 1993) Pardo Llorente, Julio Ángel; Pardo Llorente, Leandro; Menéndez Calleja, María Luisa; Taneja, I.J.
    Taneja [14] studied a unified (r, s)-entropy that includes as a particular case some of the known entropies. Based on this unified (r, s)-entropy, Pardo et al. [8] defined the average amount of information provided by an experiment X over the unknown parameter θ with prior knowledge p(θ). By using average amount of information in unified form, we compare experiments based on the Bayesian approach. Some connections with the criterion of Blackwell and Lehmann are also made. In this paper, an application of generalized entropy measures to the design and comparison of linear regression experiment is presented.
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    Sufficient fuzzy information systems
    (Fuzzy Sets and Systems, 1989) Menéndez Calleja, María Luisa; Pardo Llorente, Julio Ángel; Pardo Llorente, Leandro
    In this paper we suggest and study a new selection criterion in order to compare probabilistic information systems when the available information from them is vague, in the sense that it might be considered as fuzzy information (Tanaka, Okuda and Asai). This criterion is based on the concept of sufficient probabilistic information system established by Blackwell.
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    Tests of hypotheses for and against order restrictions on multinomial parameters based on phi-divergences
    (Utilitas mathematica, 2002) Menéndez Calleja, María Luisa; Pardo Llorente, Leandro; Zografos, Konstantinos
    Measures of phi-divergences are widely used as statistical tools for making inferences about a collection p(1), p(2),...,p(M) of multinomial parameters (Morales et al. (1995), Zografos et al. (1990), etc). Many such collections appearing in practice exhibit a trend (Robertson (1978), Lee (1987), Robertson et al. (1988) and many others cited there). In this paper tests based on measures of phi-divergence arc considered for testing a. simple null hypothesis on a. collection of multinomial parameters against an order restricted alternative and for testing an order restriction against no restriction on the collection of multinomial parameters.
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    Asymptotic distributions of weighted divergence between discrete distributions
    (Communications in statistics. Theory and methods, 1998) Franck, Ove; Menéndez Calleja, María Luisa; Pardo Llorente, Leandro
    A divergence measure between discrete probability distributions introduced by Csiszar (1967) generalizes the Kullback-Leibler information and several other information measures considered in the literature. We introduce a weighted divergence which generalizes the weighted Kullback-Leibler information considered by Taneja (1985). The weighted divergence between an empirical distribution and a fixed distribution and the weighted divergence between two independent empirical distributions are here investigated for large simple random samples, and the asymptotic distributions are shown to be either normal or equal to the distribution of a linear combination of independent chi(2)-variables.
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    On tests of symmetry, marginal homogeneity and quasi-symmetry in two-way contingency tables based on minimum phi-divergence estimator with constraints
    (Journal of Statistical Computation and Simulation, 2005) Menéndez Calleja, María Luisa; Pardo Llorente, Julio Ángel; Pardo Llorente, Leandro; Zografos, Konstantinos
    The restricted minimum phi-divergence estimator, [Pardo, J.A., Pardo, L. and Zografos, K., 2002, Minimum phi-divergence estimators with constraints in multinomial populations. Journal of Statistical Planning and Inference, 104, 221-237], is employed to obtain estimates of the cell frequencies of an 1 x 1 contingency table under hypotheses of symmetry, marginal homogeneity or quasi-symmetry. The associated phi-divergence statistics are distributed asymptotically as chi-squared distributions under the null hypothesis. The new estimators and test statistics contain, as particular cases, the classical estimators and test statistics previously presented in the literature for the cited problems. A simulation study is presented, for the symmetry problem, to choose the best function phi(2) for estimation and the best function phi(1) for testing.
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    Homogeneity/Heterogeneity Hypotheses for Standardized Mortality Ratios Based on Minimum Power-divergence Estimators
    (Biometrical journal, 2009) Pardo Llorente, Leandro; Martín Apaolaza, Níriam
    This paper analyzes the power divergence estimators when homogeneity/heterogeneity hypotheses among Standardized mortality ratios (SMRs) are taken into account. A Monte Carlo study shows that when the standard mortality rate is not external, that is it is estimated from the Sample data, these estimators have a good performance even for small sample sets and in particular the minimum chi-square estimators have a better behavior compared to the classical maximum likelihood estimators In order to make decisions under homogeneity/heterogeneity hypotheses of SNIPS we propose Some test-statistics which consider the minimum power divergence estimators Through a numerical example focused on SMRs of melanoma mortality ratios in different regions of the US, a homogeneity/heterogeneity study IS illustrated