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Autor Pardo Llorente, Leandro |
Documentos disponibles escritos por este autor (104)
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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, tha[...]![]()
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Our interest is in the problem where independent samples are drawn from two different discrete populations, possibly with a common parameter. The goal is to test hypothesis about the parameters involved in these two samples. A number of tests ar[...]![]()
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We 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 [...]![]()
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García de las Heras, Joaquín A. ; Muñoz García, Joaquín ; Muñoz Pichardo, Juan Manuel ; Pardo Llorente, Leandro | Taylor & Francis | 2006We define a new family of influence measures based on the divergence measures, in the multivariate general linear model. Influence measures are obtained by quantifying the divergence between the sample distribution of an estimate obtained with a[...]![]()
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Morales González, Domingo ; Pardo Llorente, Leandro ; Zografos, Konstantinos | Elsevier Science BV | 1998-11-15A general class of dissimilarity measures among k greater than or equal to 2 distributions and their sample estimators are considered, for mixed continuous and categorical variables. The distributional properties are studied for the location mod[...]![]()
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Morales González, Domingo ; Pardo Llorente, Leandro ; Pardo Llorente, María del Carmen | Marcel Dekker Inc | 2001The problem of introducing divergence-based statistics to test composite hypotheses related to s populations is still open when sample sizes are not equal. On the basis of likelihood divergence statistics, a statistical procedure is introduced i[...]![]()
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Morales González, Domingo ; Pardo Llorente, Leandro ; Pardo Llorente, María del Carmen ; Vadja, Igor | Tailor and Francis | 2003-06Disparities of spacings mean the phi-disparities D-phi((q) over bar (n), p(n)) of discrete hypothetical and empirical distributions g and p(n) defined by m-spacings on i.i.d. samples of size n where phi: (0, infinity) \--> HR is twice continuou[...]![]()
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The distributional properties of the weighted (h, phi)-divergence test statistics introduced in Landaburu and Pardo (2000) rely on large sample sizes for their validity. In this paper the accuracy of applying asymptotic results in cases where th[...]![]()
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The main purpose of this paper is to introduce and study the behavior of minimum (Formula presented.)-divergence estimators as an alternative to the maximum-likelihood estimator in latent class models for binary items. As it will become clear be[...]![]()
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Menéndez Calleja, María Luisa ; Morales González, Domingo ; Pardo Llorente, Leandro ; Vadja, Igor | Springer | 2001-06The paper considers statistical models with real-valued observations i.i.d. by F(x, theta (0)) from a family of distribution functions (F(x, theta); theta is an element of Theta), Theta subset of R-s, s greater than or equal to 1. For random qua[...]![]()
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We consider experiments involving the observation of a discrete random variable, or quantitative classification process and we also assume that in addition to probability of each value or class we know its "utility" or "weight" (or more precisel[...]![]()
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In 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 li[...]![]()
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Pardo Llorente, Julio Ángel ; Pardo Llorente, Leandro ; Pardo Llorente, María del Carmen | Springer Verlag | 2006-01A 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. I[...]