A review of generalized linear mixed models

We also propose a Markov Chain Monte Carlo algorithm to approx- imately sample from this posterior distribution inspired from a recent Bayesian nonparametric method for hidden

Expected progeny di fferences (EPD) for heteroskedastic error versus ho- moskedastic error models based on (a) linear mixed model analysis of birthweights and (b) cumulative probit

A marginal quasi-likelihood approach to the analysis of Poisson variables with generalized linear mixed

First, the noise density was systematically assumed known and different estimators have been proposed: kernel estimators (e.g. Ste- fanski and Carroll [1990], Fan [1991]),

In this paper, we propose a novel generative model for the blogosphere based on the random walk process that simultaneously considers both the topological and temporal properties

In this section we construct an estimator that works well whenever the distribution of X is sufficiently spherical in the sense that a positive fraction of the eigenvalues of

In this section, we adapt the results of Kappus and Mabon (2014) who proposed a completely data driven procedure in the framework of density estimation in deconvo- lution problems

We then introduce some estimates for the asymptotic behaviour of random walks conditioned to stay below a line, and prove their extension to a generalized random walk where the law