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We used the data augmentation algorithm (as described under the heading "Joint modelling imputation") to perform joint modelling imputation under the general location model and the joint prior given in the general location example (see example 4 of the Results), setting hyperparameters τ = ν = 1/2 and κ = 3/2.
Joint modelling imputation requires the specification of a parametric joint model p(x obs, x mis ∣ θ) for the complete data and a prior distribution p for parameter θ.
They did not use our transiogram joint modelling methods (Li and Zhang 2010), nor use the transition rate method for continuous-lag Markov chain models (also a transition probability joint modelling method) suggested by Carle and Fogg (1997).
In this study, a joint modelling methodology is presented.
Joint modelling of longitudinal outcome and interval-censored competing risk dropout in a schizophrenia clinical trial.
Then, the paper presents the basic joint modelling and its calibration on substructures tested experimentally.
In a joint modelling exercise a sequence of land use change scenarios was analysed.
Current methodological themes include: geostatistical analysis; spatial and spatio-temporal point processes; joint modelling of repeated measurement and time-to-event outcomes in longitudinal studies.
A combined revealed and stated preference approach, which allows joint modelling of actual and hypothetical demands, was applied.
Besides the parameterisation of obstacle influences it is also possible to use a joint modelling approach to describe obstacle induced and mesoscale changes.
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We show that the corresponding chained equations algorithm, which imputes under a set of normal linear regression models, satisfies the non-informative margins condition of Proposition 1 (and hence draws from the same joint model as joint modelling imputation).
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