Aviso: para depositar documentos, por favor, inicia sesión e identifícate con tu cuenta de correo institucional de la UCM con el botón MI CUENTA UCM. No emplees la opción AUTENTICACIÓN CON CONTRASEÑA
 

An exploratory canonical analysis approach for multinomial populations based on the phi-divergence measure

dc.contributor.authorPardo Llorente, Julio Ángel
dc.contributor.authorPardo Llorente, Leandro
dc.contributor.authorPardo Llorente, María del Carmen
dc.contributor.authorZografos, Konstantinos
dc.date.accessioned2023-06-20T09:44:27Z
dc.date.available2023-06-20T09:44:27Z
dc.date.issued2004
dc.description.abstractIn this paper we consider an exploratory canonical analysis approach for multinomial population based on the phi-divergence measure. We define the restricted minimum phi-divergence estimator, which is seen to be a generalization of the restricted maximum likelihood estimator. This estimator is then used in phi-divergence goodness-of-fit statistics which is the basis of two new families of statistics for solving the problem of selecting the number of significant correlations as well as the appropriateness of the model.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipGreek General Secretary of Research and Technology
dc.description.sponsorshipSpanish Foreign Office
dc.description.sponsorshipDGI
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/17683
dc.identifier.issn0023-5954
dc.identifier.officialurlhttp://www.kybernetika.cz/content/2004/6/757
dc.identifier.relatedurlhttp://www.kybernetika.cz
dc.identifier.urihttps://hdl.handle.net/20.500.14352/50280
dc.issue.number6
dc.journal.titleKybernetika
dc.language.isoeng
dc.page.final776
dc.page.initial757
dc.publisherKybernetika
dc.relation.projectIDBMF2003-0892.
dc.rights.accessRightsopen access
dc.subject.cdu519.237
dc.subject.keywordcanonical analysis
dc.subject.keywordrestricted minimum phi-divergence estimator
dc.subject.keywordminimum phi-divergence statistic
dc.subject.keywordsimulation
dc.subject.keywordpower divergence
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleAn exploratory canonical analysis approach for multinomial populations based on the phi-divergence measure
dc.typejournal article
dc.volume.number40
dcterms.referencesJ. Aitchison and S.D. Silvey: Maximum-likelihood estimation of parameters subject to constraints. Ann. Math. Statist. 29 (1958), 813-828. S. M. Ali and S.D. Silvey: A general class of coefficients of divergence of one distribution from another. J. Roy. Statist. Soc Ser. B 26 (1966), 131-142. E. B. A. Anderson: The Statistical Analysis of Categorical Data. Springer-Verlag, Berlin 1990. A. Basu and S. Basu:. Penalized minimum disparity methods in multinomials models. Statistica Sinica 8 (1998), 841-860. A. Basu and B. G. Lindsay: Minimum disparity estimation for continuous models. Ann. Inst. Statist. Math. 46 (1994), 683-705. A. Basu and S. Sarkar: Minimum disparity estimation in the errors-invariables model. Statist. Probab. Lett. 20 (1994), 69-73. A. Basu and S. Sarkar: The trade-off between robustness and efficiency and the effect of model smoothing. J. Statist. Comput. Simul. 50 (1994), 173-185. J. P. Benzecri: L'Analyse des Donnees. Tome 2: L'Analyse des Correspondances. Dunod, Paris 1973. M. W. Birch: A new proof of the Pearson-Fisher theorem. Ann. Math. Statist. 35 (1964), 817-824. N. A. C. Cressie and L. Pardo: Minimum (phi-divergence estimator and hierarchical testing in loglinear models. Statistica Sinica 10 (2000), 867-884. N. A. C. Cressie and L. Pardo: Model checking in loglinear models using phi-divergences and MLEs. J. Statist. Plann. Inference 103 (2002), 437-453. N. A.C. Cressie and T. R C. Read: Multinomial goodness-of-fit tests. J. Roy. Statist. Soc Ser. B 46 (1984), 440-464. N. J. Crichton and J. P. Hinde: Correspondence analysis as a screening method for indicants for clinical diagnosis. Statistics in Medicine 8 (1989), 1351-1362. I. Csiszar: Eine Informationstheoretische Ungleichung und ihre Anwendung auf den Beweis der Ergodizitat on Markhoffschen Ketten. Publ. Math. Inst. Hungar. Acad. Sci. Ser. A 8 (1963), 85-108. B. Dahdouh, J. F. Durantan, and M. Lecoq: Analyse des donnee sur l'ecologie des acridients d'Afrique de louest. Cahiers de V Analyse des Donnees 3 (1978), 459-482. M. J. R. Fasham: A comparison of nonmetric multidimensional scaling, principal components averaging for the ordination of simulated coenocicles, and coenoplanes. Ecology 58 (1977), 551-561. Z. Gilula and J. Haberman: Canonical Analysis of contingency Tables by Maximum Likelihood. J. Amer. Statist. Assoc. 81 (1986), 395, 780-788. M.J. Greenacre: Theory and Applications of Correspondence Analysis. Academic Press, New York 1984. M. Greenacre: Correspondence analysis in medical research. Statist. Meth. Medic. Res. 1 (1992), 97-117. M. J. Greenacre: Correspondence Analysis in Practice. Academic Press, London 1993. M.J. Greenacre: Correspondence Analysis of the Spanish National Health Survey. Department of Economics and Business, Universitat Pompeu Fabra, Barcelona 2002. H. O. Lancaster: The Chi-squared Distribution. Wiley, New York 1969. L. Lebart, A. Morineau, and K. Warwick: Multivariate Descriptive Statistical Analysis. Wiley, New York 1984. B. G. Lindsay: Efficiency versus robustness. The case for minimum Hellinger distance and other methods. Ann. Statist. 22 (1994), 1081-1114. G. B. Matthews and N. A. S. Crowther: A maximum likelihood estimation procedure when modelling categorical data in terms of cross-product ratios. South African Statist J. 31 (1997), 161-184. G. B. Matthews and N. A. S. Crowther: A maximum likelihood estimation procedures when modeling in terms of constraints. South African Statist. J. 29 (1995), 29-50. D. Morales, L. Pardo, and I. Vajda: Asymptotic divergence of estimates of discrete distributions. J. Statist. Plann. Inference 48 (1995), 347-369. W. C Parr: Minimum distance estimation: a bibliography. Comm. Statist. Theory Methods 10 (1981), 1205-1224. J. A. Pardo, L. Pardo, and K, Zografos: Minimum phi-divergence estimators with constraints in multinomial populations. J. Statist. Plann. Inference 104 (2002), 221-237. T. R. C. Read and N. A. C. Cressie: Goodness-of-fit Statistics for Discrete Multivariat Data. Springer, New York 1988. L. Srole, T.S. Langner, S.T. Michael, M.K. Opler, and T.A.C. Reannie: Mental Health in the Metropolis: The Midtown Manhattan Study. McGraw-Hill, New York 1962. J. Wolfowitz: Estimation by minimum distance method. Ann. Inst. Statist. Math. 5 (1953), 9-23.
dspace.entity.typePublication
relation.isAuthorOfPublication5e051d08-2974-4236-9c25-5e14369a7b61
relation.isAuthorOfPublicationa6409cba-03ce-4c3b-af08-e673b7b2bf58
relation.isAuthorOfPublication.latestForDiscovery5e051d08-2974-4236-9c25-5e14369a7b61

Download

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
PardoJulio17.pdf
Size:
2.27 MB
Format:
Adobe Portable Document Format

Collections