Although the latter are usually based on maximum likelihood estimation, the conditional mean models use non-linear least squares or quasi-maximum likelihood estimators.
However, we should note that the two-step estimation gives conditional errors of the parameters only for each step, not the marginal ones.
The estimation of conditional probabilities required in the BPN models, such as the probability of a combined failure of several components and system failure conditional on components failure, are discussed.
The latter involves non parametric estimation of conditional quantiles of a response variable from in-process data and allows process engineers to discover and visualise optimal ranges that are associated with quality improvements.
In order to quantify uncertainty and predict process robustness, a probabilistic approach, based on the likelihood ratio test with bootstrapping, is proposed which uses smoothed probability estimation of conditional probabilities.
16 The estimation of conditional correlation structures was performed with the PC-Algorithm.
Bayesian modelling allows the estimation of conditional probabilities for the existence of group differences given the data.
The sample sizes of the retrieved abstracts are large enough to support the estimation of conditional probabilities.
Since the Treated dataset contains only 1,797 samples, this drops many sites below the number of samples needed for accurate estimation of conditional K a / K s.
The elasticities that result from this estimation are conditional and assume that the change in quantity resulting from a change in price takes place with expenditure on all of the goods in the given demand system remaining constant.
