3 citations · 7 across the 5 of their papers we have counts for
5 papers
Statistical models, likelihood, penalized likelihood and hierarchical likelihood
Daniel Commenges
We give an overview of statistical models and likelihood, together with two of its variants: penalized and hierarchical likelihood. The Kullback-Leibler divergence is referred to r…
Estimating a difference between Kullback-Leibler risks by a normalized difference of AIC
D. Commenges, A. Sayyareh, L. Letenneur +2
AIC is commonly used for model selection but the precise value of AIC has no direct interpretation. We are interested in quantifying a difference of risks between two models. This…
A general dynamical statistical model with possible causal interpretation
Daniel Commenges, Anne Gegout-Petit
We develop a general dynamical model as a framework for possible causal interpretation. We first state a criterion of local independence in terms of measurability of processes invo…
Bivariate linear mixed models using SAS proc MIXED
Rodolphe Thiébaut, Hélène Jacqmin-Gadda, Geneviève Chêne +2
Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers. In this paper, we present a bivariate linear mixed model including random effec…
A Latent Process Model for Dementia and Psychometric Tests
Julien Ganiayre, Daniel Commenges, Luc Letenneur
We jointly model longitudinal values of a psychometric test and diagnosis of dementia. The model is based on a continuous-time latent process representing cognitive ability. The li…