We assume that the participants choose the health state that gives them higher utility, so this can be modeled as a conditional logistic model.
Spatial structuring in u k was modeled using a conditional autoregressive (CAR) prior structure, with spatial relationships between LGAs modeled using a simple adjacency weights matrix (Lawson et al. 2003).
The relationship between them, i.e. the probability of observing a tag count given a segment type Pr[ tagCount| segment], is modeled by a conditional probability table (CPT).
The area-level effect was modeled using a Conditional Autoregressive (CAR) prior structure, in which an adjacency matrix was specified with a weight of 1 given to adjacent hexagons and a weight of 0 given to nonadjacent hexagons.
In the presence of spatial structure in studentized residuals from the ordinary regression models, a conditional autoregressive (CAR) model was fit in R using the "spdep" package [ 40] for each set of geographical units.
For example, an independent model can be obtained in such simple special cases as a doubled haploid population, resulting from a single cross [ 2], if the dependent model is a conditional model that considers genetic variance-covariance conditioning on the marker genotypes whereas the marker genotypes are taken as random variables.
The PWP-GT model is a conditional model which allows for event dependence via stratification by event number so that different events can have different baseline hazards.
The fixed effects model made a conditional inference on the heterogeneity among the true effects, whereas the conventional random effects model treated the heterogeneity as purely random.
The aspect model introduces a conditional independence assumption, that is, d j and w i are independent conditioned on the state of the associated latent variable [15].
The second is a nonstationary model with a conditional independence restriction.
