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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Author: Martín Apaolaza, Níriam × Subject: 519.233.5 ×

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Now showing 1 - 2 of 2
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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
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    On the Asymptotic Distribution of Cook’s distance in Logistic Regression Models
    (Journal of Applied Statistics, 2009) Martín Apaolaza, Níriam; Pardo Llorente, Leandro
    It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations.