A popular model selection approach for generalized linear mixed-effects models is the Akaike information criterion, or AIC. Among others, [5] pointed out the distinction between the marginal and conditional inference depending on the focus of research. The conditional AIC was derived for the linear mixed-effects model which was later generalized by [4]. We show that the similar strategy extends...

We present novel understandings of the Gamma-Poisson (GaP) model, a probabilistic matrix factorization model for count data. We show that GaP can be rewritten free of the score/activation matrix. This gives us new insights about the estimation of the topic/dictionary matrix by maximum marginal likelihood estimation. In particular, this explains the robustness of this estimator to over-specified...

A Poisson-gamma model is introduced to account for between-subjects heterogeneity and within-subjects serial correlation occurring in longitudinal count data. The model extends the usual time-constant shared frailty approach to allow time-varying serially correlated gamma frailty whilst retaining standard marginal assumptions. A composite likelihood approach to estimation and testing for serial...

This paper considers some fundamental questions concerning marginally trapped surfaces, or apparent horizons, in Cauchy data sets for the Einstein equation. An area estimate for outermost marginally trapped surfaces is proved. The proof makes use of an existence result for marginal surfaces, in the presence of barriers, curvature estimates, together with a novel surgery construction for margina...

It has been argued in Arnold and Manjunath (2021) that the bivariate pseudo-Poisson distribution will be model of choice for data with one equidispersed marginal other over-dispersed. This is due to its simple structure, straightforward parameter estimation fast computation. In current note, we introduce effects concomitant variables on parameters explore distributional inferential aspects augm...

This note points out to applied researchers what adjustments are needed to the coefficient estimates in a random effects probit model in order to make valid comparisons in terms of coefficient estimates and marginal effects across different specifications. These adjustments are necessary because of the normalisation that is used by standard software in order to facilitate easy estimation of the...

In this paper, we adapt a mixture model originally developed for regression models with independent data for the more general case of correlated outcome data, which includes longitudinal data as a special case. The estimation is performed by a gener-alisation of the EM algorithm which we call the Expectation-Solution (ES) algorithm. In this ES algorithm the M-step of the EM algorithm is replace...