Sentence examples for an underlying variable from inspiring English sources

Tarres et al. [ 9] showed that Ducrocq's formulae [ 8], drawn from the grouped data model for survival analysis (where the value of the underlying variable is necessarily larger than 0), can be applied to an underlying variable with negative values.

However, factors that recur from different samples and conditions point to an underlying variable [ 25].

The internal validity of the frailty indicators was tested with latent class analysis, by modelling an underlying variable with three ordered categories.

The Kruskal-Wallis test is a method of testing the hypothesis that several populations have the same continuous distribution of an underlying variable.

In particular, an underlying variable N 0.86, 1) associated with the thresholds led to the same mean on the observed scale than an underlying variable N 0, 1) associated the thresholds { τ ^ L + } but resulted in a much higher variability for the L + genotype (+39%).

A threshold model of categorical traits supposes an underlying variable with a standard normal distribution and a set of thresholds which transforms this continuous variable into a multinomial variable with j ordered categories.

It was assumed that, given the location parameters β and a, the underlying variable l i of animal i is conditionally independent and distributed as (6) l i | β, a ∼ N (x i ′ β + ∑ k = 1 K z ik a k, σ e 2 ).

Wright [ 15] developed the threshold concept to map a normally distributed underlying variable to the observed ordered categorical phenotypes.

Whereas SEXTLM assumes that the effect of the EBV is additive on the underlying variable, a GDM assumes that the effect of the EBV is exponentiated to multiply the underlying variable by some constant.

For this last model, the underlying variable followed a Weibull distribution and was both a log-linear model and a grouped data model.

The difference between the logit and probit models is the assumed distribution of the underlying response; the logit model assumes a logistic and the probit model a Gaussian density for the underlying variable.