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Joint model of a longitudinal process and ...

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Joint model of a longitudinal process and informative time schedule data

Name:Personal
Bronsert, Michael Role :Text(marcrelator)
creator

Name:Personal
Shafie, Khalil Role :Text(marcrelator)
thesis advisor

Name:Personal
Mundfrom, Daniel Role :Text
committee member

Name:Personal
Schaffer, Jay Role :Text
committee member

Name:Personal
Heiny, Robert Role :Text
committee member

Name:Corporate
Applied Statistics & Research Methods Role :Text(marcrelator)
sponsor

Name:Corporate
University of Northern Colorado Role :Text(marcrelator)
degree grantor

typeOfResource
text genre(marcgt)
Thesis
Origin Information Place

University of Northern Colorado (keyDate="yes")
2009-12 Place :Text
Greeley (Colo.)
2009-12

Language :Text
English

Physical Description
144 pages
born digital

abstract
Longitudinal studies are commonly encountered in a variety of research areas in which the scientific interest is in the pattern of change in a response variable over time. These observations are traditionally scheduled prospectively and therefore common fixed time interval models for repeated measurements are adequate. Conversely, in informative schedule studies in which subsequent observations are scheduled on the basis of prior response outcomes, time between observations now becomes informative in the longitudinal process. Traditional fixed time approaches, however, are unable to utilize the informative nature of the data lessening the inferences achieved by these approaches. Therefore, the purpose of this research was the development of a joint model of a longitudinal process and informative time schedule data. Maximum likelihood estimates (MLE) for two special cases of the proposed model were obtained from Monte Carlo simulated data by employing the Multivariate Newton-Raphson optimization routine implemented in a SAS/IML call statements. Parameter estimates were determined for a few select cases of subject and observation length and included parameter estimates for rectangular and nonrectangular observation matrices. Finally, estimates obtained from PROC MIXED and from the proposed model were compared for accuracy and efficiency by examining their bias, variance, mean square error (MSE), and relative efficiency. note
Related Item :series

Related Item :thesis(displayLabel="Degree Type")
Ph.D.

Related Item :thesis(displayLabel="Degree Name")
doctoral

identifier:Local
Bronsert_unco_0161N_10023.pdf
Location (usage="primary display")
http://hdl.handle.net/10176/cogru:268

accessCondition:useAndReproduction
Copyright is held by the author.
Record Information languageOfCataloging :Text(ISO639-2B)
English :Code(ISO639-2B)
eng

note:admin
 note:bibliography
 note:thesis(displayLabel="Degree Type")
PhD note:thesis(displayLabel="Degree Name")
doctoral
Subject

Subject

Subject Name:Personal

Subject Name:Corporate

Subject

accessCondition:restrictionOnAccess
Title Information:Alternative


Subject
Gaussian

Subject
Statistics