Markovian Transition Probabilities: Estimation And Test Procedures

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dc.contributor.authorSirdari, Mahboobeh Zangeneh
dc.date.accessioned2019-01-17T07:48:59Z
dc.date.available2019-01-17T07:48:59Z
dc.date.issued2013
dc.description.abstractThe outcome variables generated from longitudinal studies are generally correlated and do pose a formidable challenge to model the repeated measures data. It is worth noting that the relationship between dependent variables as well as between dependent and explanatory variables can reveal very useful information for the policy makers and researchers in various fields. In modeling for data with dependence in outcomes as well as when the outcomes are associated with the potential explanatory variables, marginal or conditional models can be used. Most of the works found in the literature are based on marginal models and only relatively few works employ the conditional models in addressing the dependence. In a different class of models, known as the quadratic exponential form, models are developed in order to take account of underlying associations among the outcome variables. The conditional models, on the other hand, are mostly based on the Markovian assumptions. These models are gaining importance increasingly due to the underlying nature of relationships among the outcome variables generated from longitudinal data.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/7604
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectMarkovian Transition Probabilitiesen_US
dc.subjectEstimation And Test Proceduresen_US
dc.titleMarkovian Transition Probabilities: Estimation And Test Proceduresen_US
dc.typeThesisen_US
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