Person:
Pardo Llorente, Leandro

Loading...
Profile Picture
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
UCM identifierScopus Author IDWeb of Science ResearcherIDDialnet IDGoogle Scholar ID

Search Results

Now showing 1 - 2 of 2
  • Item
    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.
  • Item
    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.