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dc.creatorBhat, Chandra R.en
dc.date.accessioned2014-09-29T20:37:16Zen
dc.date.available2014-09-29T20:37:16Zen
dc.date.issued2014-08-01en
dc.identifier.urihttp://hdl.handle.net/2152/26173en
dc.description.abstractThis paper formulates a generalized heterogeneous data model (GHDM) that jointly handles mixed types of dependent variables—including multiple nominal outcomes, multiple ordinal variables, and multiple count variables, as well as multiple continuous variables—by representing the covariance relationships among them through a reduced number of latent factors. Sufficiency conditions for identification of the GHDM parameters are presented. The maximum approximate composite marginal likelihood (MACML) method is proposed to estimate this jointly mixed model system. This estimation method provides computational time advantages since the dimensionality of integration in the likelihood function is independent of the number of latent factors. The study undertakes a simulation experiment within the virtual context of integrating residential location choice and travel behavior to evaluate the ability of the MACML approach to recover parameters. The simulation results show that the MACML approach effectively recovers underlying parameters, and also that ignoring the multidimensional nature of the relationship among mixed types of dependent variables can lead not only to inconsistent parameter estimation, but also have important implications for policy analysis.en
dc.publisherThe University of Texas at Austinen
dc.subjectLatent factorsen
dc.subjectbig data analyticsen
dc.subjecthigh dimensional dataen
dc.subjectMACML estimation approachen
dc.subjectmixed dependent variablesen
dc.subjectstructural equations modelsen
dc.subjectintegrated land usetransportation modelingen
dc.subjectfactor analysisen
dc.titleA New Generalized Heterogeneous Data Model (GHDM) to Jointly Model Mixed Types of Dependent Variablesen
dc.typeTechnical reporten
dc.description.departmentCivil, Architectural, and Environmental Engineeringen


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