Person:
Pardo Llorente, Leandro

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

Identifiers

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Search Results

Now showing 1 - 10 of 10

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 Carmen
The 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.
Model Selection in a Composite Likelihood Framework Based on Density Power Divergence
(Entropy, 2020) Castilla González, Elena María; Martín Apaolaza, Nirian; Pardo Llorente, Leandro; Zografos, Konstantinos
This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α. After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion.
Minimum phi-divergence estimator and hierarchical testing in loglinear models
(Statistica Sinica, 2000) Cressie, Noel A.; Pardo Llorente, Leandro
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 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.
Confidence sets and coverage probabilities based on preliminary estimators in logistic regression models
(Journal of Computational and Applied Mathematics, 2009) Menéndez Calleja, María Luisa; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen
In this paper we present recentered confidence sets for the parameters of a logistic regression model based on preliminary minimum phi-divergence estimators. Asymptotic coverage probabilities are given as well as a simulation study in order to analyze the coverage probabilities for small and moderate sample sizes.
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 Carmen
In 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.
Phi-divergence-type test for positive dependence alternatives in 2 x k contingency tables
(Advances in distribution theory, order statistics, and inference, 2006) Pardo Llorente, Leandro; Menéndez Calleja, María Luisa; Balakrishnan, N.; Castillo, Enrique; Sarabia, José Maria
In this chapter, we consider 2 x k contingency tables and derive a new family of test statistics for detecting positive dependence in them. The family of test statistics introduced here is based on the phi-divergence measures of which the likelihood ratio test is a special case.
Bahadur efficiency of the phi-divergence test statistic
(Soft methodology and ramdom information systems, 2004) Pardo Llorente, Leandro; Lopez Diaz, M.C.; Angeles Gil, M.; Grzegorzewski , P.; Hryniewicz, O.; Lawry, J.
In this paper the Bahadur efficiency in the family of phi-divergence statistics for goodness of fit in multinomial populations is studied.
A simulation study of a nested sequence of binomial regression models
(Statistics, 2007) Pardo Llorente, Julio Ángel; Pardo Llorente, Leandro; Pardo Llorente, María del Carmen
The inference problem we consider is that of model choice from a nested sequence of binomial regression models. The approach we take is to test successively, from most general to most specific, the corresponding sequence of composite hypotheses. This approach is based on the very general class of divergence measures, the phi-divergence. An approximation to the power function of the new family of test statistics proposed is obtained. An extensive simulation study is carried out by obtaining new test statistics that are a good alternative to the traditional loglikelihood test statistic.
Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator
(Entropy, 2017) Castilla González, Elena María; Martín, Nirian; Pardo Llorente, Leandro; Zografos, Konstantinos
In this paper, a robust version of the Wald test statistic for composite likelihood is considered by using the composite minimum density power divergence estimator instead of the composite maximum likelihood estimator. This new family of test statistics will be called Wald-type test statistics. The problem of testing a simple and a composite null hypothesis is considered, and the robustness is studied on the basis of a simulation study. The composite minimum density power divergence estimator is also introduced, and its asymptotic properties are studied.
Influence measures based on Cressie-Read divergence measures in multivariate linear model
(Communications in statistics.Theory and methods, 2006) García de las Heras, Joaquín A.; Muñoz García, Joaquín; Muñoz Pichardo, Juan Manuel; Pardo Llorente, Leandro
We 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 all the observations and the sample distribution of the same estimate obtained without any observation. This approach is applied to best linear unbiased estimates of estimable functions. Therefore, these diagnostics can be applied to every statistical multivariate technique that can be formulated like this kind of model. Some examples are considered to clarify the applicability of the introduced diagnostics.