… distributions. In this paper we obtain joint distributions, and hence conditional distributions,
of … Numerical examples illustrate what insight these conditional distributions may provide …

… On the other hand, distribution fitting, given an algebraic … to performing distribution fitting with
maximum likelihood (ML) and a … The likelihood of the data set T is the joint probability of the …

… We fit them by improving a maximum likelihood algorithm … the joint distribution and the
marginal distributions of every … -order marginal distributions and the joint distribution would have …

… likelihood method for joint models … likelihood corresponding to the joint distribution of the
observed time-to-event and longitudinal outcomes { T i , δ i , y i } . To define this joint distribution …

… A second finding of this article is to point out the theoretical challenges regarding efficiency
and the asymptotic distributions of the parametric estimators in the joint modeling framework. …

… has been developed for fitting statistical distributions to air pollution data using maximum
likelihood estimation. … The likelihood function is defined as the joint density of the ungrouped …

… We discuss maximum likelihood methods for fitting a broad … -sample distributions for maximum
likelihood estimators of pa… both the joint distributions and the marginal distributions. Their …

… When fitting the joint model without the interaction terms, … model and maximum likelihood
estimation for joint analysis of … The distribution of the random effects in our model is assumed …

… In this section, we compare to a Wishart distribution with fitted parameters ν eff and |$\… ,
whereas our exact likelihood automatically produces the full joint distribution between all …

… likelihood approach to inference in joint models requiring only that the random effects have
distribution … We fitted the full model using the SNP likelihood-based approach with K = 0,1,2,3,…