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dc.creatorFennell, Mary L
dc.creatorTuma, Nancy Brandon
dc.creatorHannan, Michael T
dc.date.accessioned2015-08-15T22:49:29Z
dc.date.available2015-08-15T22:49:29Z
dc.date.issued2015-08-15
dc.identifier.urihttp://hdl.handle.net/1969.1/154800
dc.description.abstractThe authors address four sources of indeterminacy in maximum likelihood estimation (MLE) for multivariate modeling of change using panel data: censoring, caused by changes that occur after the observation period ends; small sample size; interacting censoring with sample size; and collinearity among causal variables. They explore the issues with simulations and conclude that MLE estimates are generally efficient except when censoring is extreme, and efficiency is only slightly affected by collinearity among independent variables. Related publications include Tuma and Hannan (1979) and Tuma, Hannan, and Groeneveld (1979).en
dc.language.isoen_US
dc.relation.ispartofseriesTechnical Report Stanford Sociology;#59
dc.rightsAttribution 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectstatistical censoringen
dc.subjectmaximum likelihood estimationen
dc.titleQuality of Maximum Likelihood Estimates of Parameters in a Log-Linear Rate Modelen
dc.typeTechnical Reporten
local.departmentSociologyen


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Attribution 3.0 United States
Except where otherwise noted, this item's license is described as Attribution 3.0 United States