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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2003 - 20094
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Author: Pardo Llorente, María del Carmen × Subject: 519.21 ×

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Now showing 1 - 4 of 4
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    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.
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    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.
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    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.
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    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.