A review of generalized linear mixed models

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

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

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

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

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