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PC program for growth prediction in the two-stage polynomial growth curve model

dc.contributor.authorGuo, Ingrid Y.en_US
dc.contributor.authorSchneiderman, Emet D.en_US
dc.contributor.authorKowalski, Charles J.en_US
dc.contributor.authorWillis, Stephen M.en_US
dc.date.accessioned2006-04-10T18:21:22Z
dc.date.available2006-04-10T18:21:22Z
dc.date.issued1994-02en_US
dc.identifier.citationGuo, Ingrid (Ying-Yueh) Y., Schneiderman, Emet D., Kowalski, Charles J., Willis, Stephen M. (1994/02)."PC program for growth prediction in the two-stage polynomial growth curve model." International Journal of Bio-Medical Computing 35(1): 39-46. <http://hdl.handle.net/2027.42/31789>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B7GH2-4C4WG3T-2S/2/00832683ab8b6a9ea5f7f766e73f4df1en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/31789
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=8175207&dopt=citationen_US
dc.description.abstractWe consider the problem of growth prediction in the context of the two-stage (or random coefficients) one-sample polynomial growth curve model and provide a PC program, written in GAUSS386i, to perform the associated computations. The problem considered is that of estimating the value of the measurement under consideration for a `new' individual at the Tth time point given measurements on that individual at T - 1 previous points in time and the values of the measurement on N `similar' individuals at all T time points. The times of measurement t1, t2,..., tT need not be equally spaced, but we assume that each of the N individuals comprising the normative sample were measured at these times. The method and the program are illustrated using the data set previously considered (Schneiderman and Kowalski, Am J Phys Anthrop, 67 (1985) 323-333) consisting of mandibular ramus height measurements (in mm) for 12 male rhesus monkeys at T = 5 yearly intervals (coded 1, 2, 3, 4, and 5). Results are compared with those obtained under a less restrictive set of assumptions concerning the covariance matrix of the observations than is made in the context of the two-stage model. It is seen that the accuracies of prediction of the two methods, for this and other data sets, are quite close, suggesting that the less restrictive model may be preferred in many situations.en_US
dc.format.extent538735 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titlePC program for growth prediction in the two-stage polynomial growth curve modelen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelWest European Studiesen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelHumanitiesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biologic and Materials Science, The University of Michigan, Ann Arbor, MI 48109, USAen_US
dc.contributor.affiliationotherDepartment of Public Health Sciences, Baylor College of Dentistry, 3302 Gaston Avenue, Dallas, TX 75246, USAen_US
dc.contributor.affiliationotherDepartment of Oral and Maxillofacial Surgery and Pharmacology, Baylor College of Dentistry, 3302 Gaston Avenue, Dallas, TX 75246, USAen_US
dc.contributor.affiliationotherDepartment of Oral and Maxillofacial Surgery and Pharmacology, Baylor College of Dentistry, 3302 Gaston Avenue, Dallas, TX 75246, USAen_US
dc.identifier.pmid8175207en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/31789/1/0000730.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0020-7101(94)90047-7en_US
dc.identifier.sourceInternational Journal of Bio-Medical Computingen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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