Sentence examples for maximum likelihood parametric from inspiring English sources

On the assumption that the main noise sources in flutter tests are related to turbulence and that non-linearities may exist in the aeroelastic system, it is shown that the maximum likelihood parametric identification method is more capable than non-parametric methods of identifying experimentally mentally a flexural-torsional flutter of a wing model excited by three different forcing functions.

We performed maximum likelihood parametric exponential survival analyses to estimate 1/ qk, that is the average time for all individuals to transition from one CD4+ category to the next.

Note that Z can be either discrete or continuous, and that the approach easily incorporates observed confounders C. In fact, by following similar steps as above, one can show that (10) and the marginal ETT is given by Estimation could then proceed by fitting using standard maximum likelihood, parametric models for Pr Y = 1 |A, C) and E(Z|A, C) and plugging the latter into equation 10.

In contrast to all fungi and animals in our study, E. cuniculi contained no sequences from Groups 1 or 2. Maximum likelihood non-parametric bootstrapping is not ideal for large datasets; bootstrap values decrease as the taxon number increases [ 32] and the fast bootstrap methods without branch-swapping typically applied to large datasets may not be reliable at nodes with weak support [ 33].

Estimation is by marginal maximum likelihood where a parametric population distribution for the random change point is combined with a non-parametric mixing distribution for other random effects.

The chosen model, K81uf + pinvar + Γ, was used for all maximum likelihood analyses and parametric hypothesis testing.

We use a single chain and non-informative prior for all imputations, and expectation-maximization (EM) algorithm to find maximum likelihood estimates in parametric models for incomplete data and derive parameter estimates from a posterior mode.

Using the probit model and maximum likelihood estimation, a parametric ROC regression analysis of DHEAS screening on HPAS was carried out and two covariates, age and sex, were adjusted for.

Hence parametric maximum likelihood estimation is potentially more useful than non-parametric estimation since the unconditional distribution is of interest for pharmacovigilance purposes [ 18, 20].

We followed the graphical procedure for checking goodness-of-fit for right-truncated data suggested by Lawless (2003) that is based on the non-parametric maximum likelihood estimator and consists in plotting the conditional fitted parametric survivals together with the non-parametric estimation [ 36].

As one can only estimate a conditional distribution function in a non-parametric setting, the non-parametric maximum likelihood estimator is of rather little interest for pharmacovigilance people.