… , finite mixture models and local likelihood density estimation … which allows for a robust and
reliable detection of the modality of … (ii) Secondly, a simple greedy knot deletion algorithm is …

… is straightforward, this is not a good choice here. As explained by Benaglia et al. (2009), the
… parametric mixture models and their maximum likelihood estimation via an EM algorithm …

… likelihood under a general, or nonparametric, mixture model … based algorithm is more reliable
than using the EM algorithm … computes the NPMLEs using the EM algorithm. So, we shall …

… ˆηG for ηG is studied in semiparametric theory, see eg, [1]. One … , we do not know how good
is its performance, beyond the … nonparametric maximum likelihood for mixture models: A …

… to show that the performance of the algorithm is at least as good as that of the effective EM
… maximum likelihood estimation of the semi-parametric mixture of partial linear models (…

… for the semiparametric Maximum Likelihood approach. … provides a good indicator both
for the choice of the algorithm … Maximizing generalized linear mixed model likelihoods with …

… are estimated by the maximum likelihood estimators (MLE). This … We also propose a
semiparametric EM algorithm to estimate the … (2001) to the semiparametric mixture model …

… deterministic algorithm capable of estimating the components of a nonparametric mixture
model … 3, where the estimates were good. It seems that initialization plays a key role in the final …

… study shows the good performance of the algorithms in terms … 2006) and is a fully conditional
mixture model where both the … semi-parametric probability density (or mass) function: …

… component to be log-concave, which contains a very broad family of densities, we develop
a semiparametric maximum likelihood estimator and propose an EM algorithm to compute it. …