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Estadística multivariada: inferencia y métodos

dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.authorDíaz Monroy, Luis Guillermo
dc.contributor.otherHernández Quitián, Margoth
dc.date.accessioned2021-08-10T17:01:26Z
dc.date.available2021-08-10T17:01:26Z
dc.date.issued2007
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/79907
dc.descriptionTablas e ilustraciones
dc.description.abstractLa segunda edición tiene en cuenta al lector para quien se pensó debe ir dirigido, es decir cualquier persona que posea conocimientos básicos de matemáticas y estadística. Aunque se ha mantenido la estructura de la primera edición, ésta ha sido sometida a una revisión exhaustiva, cuyo resultado ha permitido la detección y corrección de algunas ambigüedades, la corrección de errores ortográficos y de edición, los cuales fueron advertidos al autor por juiciosos lectores. Algunos ejemplos fueron desarrollados con más detalle y sencillez. Para cada uno de los once capítulos y los dos anexos se ha incluido la sintaxis del paquete estadístico R, con el cual se desarrollan los cálculos, las tablas y las gráficas de algunos de los ejemplos contenidos en el respectivo capitulo. (Texto tomado de la fuente).
dc.format.extentxvii, 570 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.relation.ispartofseriesColección textos;
dc.rightsDerechos Reservados al Autor, 2007
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
dc.titleEstadística multivariada: inferencia y métodos
dc.typeLibro
dcterms.audienceGeneral
dc.type.driverinfo:eu-repo/semantics/book
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.description.notesISBN de la versión impresa 9789587011951
dc.contributor.graphicaldesignerKratzer, Andrea
dc.description.editionSegunda edición
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentSede Bogotá
dc.publisher.placeBogotá, Colombia
dc.relation.citationeditionSegunda edición
dc.relation.referencesAlfenderfer, Mark S., and Blashfield, Roger., Cluster Analysis, Series: Quantitative Applications in the Social Sciences, Sage Publications, Inc., Beverly Hills, (1984).
dc.relation.referencesAnderson, T. W., An Introduction to Multivariate Statistical Analysis, John Wiley and Sons., New York, 1984.
dc.relation.referencesAnderson, T. W., Asymptotic Theory for Principal Component Analysis, The Annals of Mathematical Statistics, Vol. 34, 122-148, 1963.
dc.relation.referencesAndrews, D. F., Plots of high-dimensional data, Biometrics, Vol. 28, 125-136, 1972.
dc.relation.referencesAndrews, D. F., Gnanadesikan, R., and Warner, J. L., Methods for Assessing Multivariate Normality. In P. R. Kishnaiah (Ed.) Multivariate Analysis, Vol. III, 95-116, Academic Press, New York, 1973.
dc.relation.referencesArnold, Steven F., The Theory of Linear Models and Multivariate Analysis, John Wiley and Sons, 1981.
dc.relation.referencesBartlett, M. S., Properties of Sufficiency and Statistical Tests, Proceedings of the Royal Society of London, Vol. 160, 268-282, 1937.
dc.relation.referencesBartlett, M. S., A note on test of significance in multivariate analysis, Proceedings of the Cambridge Philosophical Society, Vol. 35, 180-185, 1939.
dc.relation.referencesBartlett, M. S., Anote on multiplying factors for various chi-squared approximations, Journal of the Royal Statistical Society, Series B, Vol. 16, 296-298, 1954.
dc.relation.referencesBenzecri, J. P., Cours de Linguistique Mathématique, Publication multigraphiée, (Faculté des Sciences de Rennes). 1964.
dc.relation.referencesBenzecri, J. P., L’Analyse des Données, Tomo 1: La Taxinomie, Tomo 2: L’Analyse des Correspondances, Dunod, Paris, 1973.
dc.relation.referencesBenzecri, J. P., Histoire et Préhistoire de l’Analyse des Données L’Analyse des Données., Les Cahiers de Analyse des Donées Dunod, Paris, 1976.
dc.relation.referencesBiscay, R., Valdes, P. and Pascual, R., Modified Fisher’s linear discriminant function with reduction of dimensionality, Journal of Statistical Computation and simulation, Vol. 36, 1-8, 1990.
dc.relation.referencesBorg, Ingwer., and Groenen, Patrick, Modern Multidimensional Scaling, Springer, New York. 1997.
dc.relation.referencesBox, G. E. P., A general distribution theory for a class of likelihood criteria, Biometrika, Vol. 36, 317-346, 1949.
dc.relation.referencesBox, G. E. P. and Cox, D. R., An analysis of transformations, Journal of the Royal Statistical Society, Series B, Vol. 26,211-252, 1964.
dc.relation.referencesBuck, S. F. A., A Method of estimation of missing values in multivariate data suitable for use with an electronic computer, Journal of the Royal Statistics Society, Series B, Vol. 22, 302-307, 1960.
dc.relation.referencesCatell, R. B., The screen test for the number of factors, Multivariate Behavioral Research, Vol. 1, 140-161, 1966.
dc.relation.referencesChatfield, C. and Collins, A. J., Introduction to Multivariate Analysis Chapman and Hall, New York. 1986.
dc.relation.referencesCherkassky, Vladimir., Friedman, Jerome H. and Wechsler, Harry. From Statistics to Neural Networks, Theory and Pattern Recognition Applications, Springer, Berlin, 1993.
dc.relation.referencesChernoff, Herman., Using faces to represent points in k-dimensional space graphically, Journal of the American Statistics Association, Vol. 68, 361-368, 1973.
dc.relation.referencesClifford, H. and Stephenson, W., Introduction to Numerical Taxonomic, Academic Press, New York. 1975.
dc.relation.references[23] Crisci, Jorge Víctor y López, María Fernanda., Introducción a la Teoría y Práctica de la Taxonomía Numérica, Secretaría General de la OEA, Washington, D. C., 1983.
dc.relation.referencesCox, Trevor F. and Cox, Michael A. A., Multidimensional Scaling, Chapman and Hall, London. 1994.
dc.relation.referencesCrowder, M. J. and Hand, D. J., Analysis of Repeated Measures, Chapman and Hall, New York. 1990.
dc.relation.referencesD’agostino, R. B. and Pearson, E. S., Test for deperture from Normality. Empirical Results for the Distributions of b2 and √b1, Biometrika, Vol. 60, 613-622; correction 61, 647, 1973.
dc.relation.referencesDíaz, Luis Guillermo y López, Luis Alberto, Tamaño de muestra en diseño experimental, Memorias III Simposio de Estadística Muestreo, Universidad Nacional de Colombia, Santafé de Bogotá, D. C., 132- 154, 1992.
dc.relation.references[28] Dillon, William R. and Goldstein, Matthew., Multivariate Analysis, Methods and Applications John Wiley and Sons, New York, 1984.
dc.relation.referencesDiday, E., Optimisation en classification automatique et reconnnaisance des formes, Revue Française de Recherche Opérationnelle, Vol. 3, 61-96, 1972.
dc.relation.referencesDiday, E., Classification automatique séquentielle pour grands tableaux, Revue Française de Recherche Opérationnelle, Vol. 9, 1- 29, 1974.
dc.relation.referencesEfron, B. and Tibshirani, R., An Introduction to the Bootstrap, Chapman and Hall, London, 1993.
dc.relation.referencesEscofier, Brigitte. et Pages, Jérome., Analyses factorielles simples et multiples, Dunod, Paris, 1990.
dc.relation.referencesEveritt, Brian S., Cluster Analysis, Heineman Educational Books, London, 1980.
dc.relation.referencesEveritt, Brian S. and Dunn, Graham., Applied Multivariate Data Analysis, Edward Arnold Books, New York, 1991.
dc.relation.referencesForgy, E. W., Cluster analysis of multivariate data: efficiency versus interpretability of classifications, Biometrics, 768, Vol. 21, 1965.
dc.relation.referencesFreund, Rudolf J., Litell, Ramon C. and Spector, Philip C., SAS system for linear models, SAS Institute Inc., Cary, NC., 1986.
dc.relation.referencesGiri, Narayan C., Multivariate Statistical Inference, Academic Press, New York, 1977.
dc.relation.referencesGnanadesikan, R. Methods for Statistical Analysis of Multivariate Observations, John Wiley and Sons, New York., 1997.
dc.relation.referencesGnanadesikan, R. and Kattenring, J. R., Robust stimates, residulas and outlier detection with multiresponse data, Biometrics, 81-124, 1972.
dc.relation.referencesGordon, A. D., A Review of hierarchical Classification, Series A Journal of the Royal Statistical Society, 150-119, 1937.
dc.relation.referencesGraybill, Franklyn A., Theory and Application of the Linear Model, Duxbury Press, Massachusetts, 1976.
dc.relation.referencesGorsuch, Richard L., Factor Analysis, Lawrence Erlbaum Associates, Publishers, London, 1983.
dc.relation.referencesHarville, David A., Introduction to Matrix Algebra from a Statistician’s Perspective, Springer, New York, 1997.
dc.relation.referencesHogg, Robert V. and Craig, Allent T., Introduction to Mathematical Statistics, Macmillan Publishing Co. Inc., New York, 1978.
dc.relation.referencesHotelling, H., The generalization of Student’s ratio, Annals of Mathematical Statistics, Vol. 2, 360-378, 1931.
dc.relation.referencesJobson, J. D., Applied Multivariate Data Analysis, Volume I: Regression and Experimental Design, Springer, New York, 1992.
dc.relation.referencesJobson, J. D., Applied Multivariate Data Analysis, Volume II: Categorical and Multivariate Methods, Springer, New York, 1992.
dc.relation.referencesJohnson, Richard and Wicher, Dean W., Applied Multivariate Statistical Analysis, Prentice Hall, Inc., New Jersey, 1998.
dc.relation.referencesJöreskog, K. G., Some contributions to maximum likelihood factor analysis, Psychometrika, Vol. 32,443-482, 1967.
dc.relation.referencesKaiser, K. G., The varimax criteriom for analytic rotation in factor analysis, Psychometrika, Vol. 23, 187-200,1958.
dc.relation.referencesKaiser, K. G., Some contributions to maximum likelihood factor analysis, Psychometrika, Vol. 32, 443-482, 1967.
dc.relation.referencesKruskal, J. B., and Wish, M., Multidimensional Scaling, Sage Publications, Beverly Hills, CA., 1978.
dc.relation.referencesKrzanowski, W. J. and Marriot, F. H. C., Multivariate Analysis. Part 1 Distributions, Ordination and Inference, Edward Arnold, London, 1994.
dc.relation.referencesKrzanowski, W. J. and Marriot, F. H. C., Multivariate Analysis. Part 2 Classification, covariance structures and repeated measurements, Edward Arnold, London, 1995.
dc.relation.referencesLawley, D. N., A generalization of Fisher’s z test, Biometrika, Vol. 30, 180-187, 1938.
dc.relation.referencesLawley, D. N., Some new results in maximum likelihood factor analysis, Proceedings of the Royal Society of Education, Vol. 67, 256-264, 1967.
dc.relation.referencesLebart, Ludovic, Morineau, Alan, Fénelon, Jean-Pierre, Tratamiento Estadístico de Datos, Marcombo-Boixareu Editores, Barcelona. 1985.
dc.relation.referencesLebart, Ludovic, Morineau, Alan, Piron, Marie, Statistique Exploratoire Multidimensionnelle, Dunod, Paris, 1995.
dc.relation.referencesLebart, Ludovic, Morineau, Alan, and Warwick, Kenneth M., Multivariate Descriptive Statistical Analysis, John Wiley and Sons, New York, 1984.
dc.relation.referencesLee, Kerry L., Multivariate Test for Cluster, Journal of the American Statistical Association, Vol. 74, 708-714, 1979.
dc.relation.referencesLittle, R. J. A. and Rubin, D. B., Statistical Analysis with Missing Data John Wiley and Sons, New York, 1987.
dc.relation.referencesLou, Sheldon., Jiang, Jiong., and Keng, Kenneth, Clustering Objects Generated by Linear Regression Models, Journal of the American Statistical Association, Vol. 88, 1356-1362, 1993.
dc.relation.referencesMaclachlan, Geoffrey J., Discriminant Analysis and Statistical Pattern Recognition, John Wiley and Sons, New York, 1992.
dc.relation.referencesManly, Bryan F. J., Multivariate Statistical Methods, A primer, Chapman and Hall, New York, 2000.
dc.relation.referencesMardia, K. V., Measures of multivariate skewness and kurtosis with applications, Biometrika, Vol. 57, 519-530, 1970.
dc.relation.referencesMardia, K. V., Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies, Sankhya B, Vol. 36, 115-128, 1974.
dc.relation.referencesMardia, K. V., Kent, J. T., and Bibby, J. M., Multivariate Analysis, Academic Press, New York, 1979.
dc.relation.referencesMason, R. L., Tracy, N. D. and Young, J. C., Decomposition of T2 for multivariate control chart interpretation, Journal of Quality Technology, Vol. 27 (2), 157-158, 1995.
dc.relation.referencesMijares, Tito A., The normal approximation to the Bartlett- NandaPillai trace test in multivariate analysis, Biometrika, Vol. 77, 230- 233, 1990.
dc.relation.referencesMilligan, G. W. and Cooper, M. C., An examination of procedures for determining the number of cluster, Psychometrika, Vol. 50, 159-179, 1985.
dc.relation.referencesMood, Alexander M., Graybill, Franklyn A. and Boes, Duane C., Introduction to the Theory of Statistics, Mc Graw Hill Book Company, 1982.
dc.relation.referencesMorrison, Donald F., Multivariate Statistical Methods, Mc Graw Hill Book Company, New York, 1990.
dc.relation.referencesMuirhead, Robb J. Aspects of Multivariate Statistical Theory, John Wiley and Sons, New York, 1982.
dc.relation.referencesNagarsenker, B. N. and Pillai, K. C. S., Distribution of the likelihood ratio for testing Σ=Σ0, μ = μ0, Journal of multivariate analysis,114-122, Vol. 4, 1974.
dc.relation.referencesNanda, D. N., Distribution of the sum of roots of the determinant equation under a certain condition, Annals of Mathematical Statistics, Vol. 21, 432-439, 1950.
dc.relation.referencesPardo, Campo Elías., Análisis de la Aplicación del Método de Ward de Clasificación Jerárquica al Caso de Variables Cualitativas, Universidad Nacional de Colombia. Tesis de Magister en Estadística, Santafé de Bogotá, D. C., 1992.
dc.relation.referencesPeck, Roger., Fisher, Lloyd., and Van, John., Approximate confidence intervals for the number of cluster, Journal of the American Statistical Association, Vol. 84, 184-191, 1989.
dc.relation.referencesPeña S., Daniel, Estadística modelos y métodos. Fundamentos, Alianza Universitaria Textos, Madrid, 1998.
dc.relation.referencesPillai, K. C. S., Some new test criteria in multivariate analysis, Annals of Mathematical Statistics, Vol. 26, 117-121, 1955.
dc.relation.referencesPla, Laura E., Análisis Multivariado: Método de Componentes Principales, Secretaría General de la OEA, Washington, D. C., 1986.
dc.relation.referencesRencher, Alvin C., Methods of Multivariate Analysis, John Wiley and Sons, New York, 1995.
dc.relation.referencesRencher, Alvin C., Multivariate Statistical Inference and Applications, John Wiley and Sons, New York, 1998.
dc.relation.referencesRohatgi, Vijay K., Statistical Inference, John Wiley and Sons, New York, 1984.
dc.relation.referencesRoussas, George G., A First Course in Mathematical Statistics, Addison-Wesley Publishing Company, Massachusetts, 1973.
dc.relation.referencesRoy, S. N., On a heuristic method of test construction and its use in multivariate analysis, Annals of Mathematical Statistics, Vol. 24, 220-238, 1953.
dc.relation.referencesRoy, S. N., Some Aspects of multivariate Analysis, John Wiley and Sons, New York, 1957. [87] Ruiz-Velazco, S., Asympototic efficiency of logistic regression relative to linear discriminant analysis, Biometrika, Vol. 78, 235-243, 1991.
dc.relation.referencesSaporta, Gilbert., Probabilités Analyse des Données et Statistique, Technip, Paris, 1990.
dc.relation.referencesSAS Institute Inc., SAS/STAT User’s Guide, SAS Institute Inc., Cary N. C., 2001.
dc.relation.referencesSchott, James R., A test for a specific principal component of a correlation matrix, Journal of the American Statistical Association, Vol. 86, 747-751, 1991.
dc.relation.referencesSearle, S. R., Matrix Algebra Useful for Statistics, John Wiley and Sons, New York. 1990.
dc.relation.referencesSeber, G.A.F., Multivariate observations, Jonhn Wiley and Sons, New York, 1984.
dc.relation.referencesSharma, Subhash, Applied Multivariate Techniques, Jonhn Wiley and Sons, New York, 1996.
dc.relation.referencesSilverman, B. W. Density Estimation for Statistics and Data Analysis, Chapman and Hall, New York, 1986.
dc.relation.referencesSokal, R. and Michener, C. D., A statistical method for evaluating systematic relationship, University of Kansas Scientific Bulletin, 1958.
dc.relation.referencesTakane, Y., Young, F. W., and Leeuw, J., Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features, Psychometrika, Vol. 42, 7-67, 1977.
dc.relation.referencesThompson, Paul A., Correspondence analysis in statistical package programs, The American Statistician, Vol. 49, 310-316, 1995.
dc.relation.referencesTiku, M. L., Tables of the power of the F-test, Journal of the American Statistical Association, Vol. 62, 525-539, 1967.
dc.relation.referencesTorres, Luz Gloria, Ni˜no, Luis F. y Hern´andez, Germ´an., Redes Neuronales, X Coloquio Distrital de Matem´aticas y Estad´ıstica, Santaf´e de Bogot´a, 1993.
dc.relation.referencesTukey, J. W., On the Comparative Anatomy of Transformations, Annals of Mathematical Statistics, Vol. 28, 602-632, 1957.
dc.relation.referencesVelilla, S., and Barrio, J. A., A discriminant rule under transformation, Technometrics, Vol. 36, 348-353, 1994.
dc.relation.referencesWard, J., Approximate confidence intervals for the number of cluster, Journal of the American Statistical Association, Vol. 58, 236-224, 1963.
dc.relation.referencesWelch, B. L., The significance of the difference between two means when the population variances are unequal, Biometrika, Vol. 29, 350- 360,1937.
dc.relation.referencesWelch, B. L., The generalization of “Student” problem when several different population variances are involved, Biometrika, Vol. 34, 28- 35, 1947.
dc.relation.referencesWilcox, Rand R. Introduction to Robust Stimation and Hipothesis Testing, Academic Press, New York, 1997.
dc.relation.referencesYager, R. R., Ovchinnikov, S., Togn, R. M., and Nguyen, H. T., Fuzzy Sets and Applications. Selected Papers by L. A. Zadeh, John Wiley and Sons, New York, 1987.
dc.relation.referencesYan, S. S., and Lee, Y., Identification of a multivariate outlier, Annual Meeting of the American Statistical Association, 1987.
dc.relation.referencesYeo, In-Know and Johnson, Richard A. A new family of power transformations to improve normality or symmetry, Biometrika, 2000.
dc.relation.referencesZadeh, Lotfi A., Fuzzy Sets, Information and Control, 338-353, 1965.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembDiseño experimental
dc.subject.lembEstadística matemática
dc.subject.lembProbabilidades
dc.subject.lembAnálisis estadístico multivariable
dc.subject.proposalDistribuciones multivariantes
dc.subject.proposalInferencia
dc.subject.proposalConceptos estadísticos
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dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/LIB
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Atribución-NoComercial-SinDerivadas 4.0 InternacionalEsta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.Este documento ha sido depositado por parte de el(los) autor(es) bajo la siguiente constancia de depósito