On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg

A geoadditive Bayesian latent variable model for Poisson indicators

We introduce a new latent variable model with count variable indicators, where usual linear parametric effects of covariates, nonparametric effects of continuous covariates and spatial effects on the continuous latent variables are modelled through a geoadditive predictor. Bayesian modelling of nonparametric functions and spatial effects is based on penalized spline and Markov random field priors. Full Bayesian inference is performed via an auxiliary variable Gibbs sampling technique, using a recent suggestion of Frühwirth-Schnatter and Wagner (2006). As an advantage, our Poisson indicator latent variable model can be combined with semiparametric latent variable models for mixed binary, ordinal and continuous indicator variables within an unified and coherent framework for modelling and inference. A simulation study investigates performance, and an application to post war human security in Cambodia illustrates the approach

Diversité ethno-culturelle et différentiel de pauvreté multidimensionnelle au Cameroun

Peu de recherches ont concilié le caractère multidimensionnel de la pauvreté avec le conditionnement culturel des populations pour orienter les politiques. La démarche de la MES (Modélisation en Équations Structurelles) à travers sa technique de comparaison de modèles nichés a permis de formuler et de tester les hypothèses de recherche. Les résultats montrent que les différences de niveau observées sur les dimensions de pauvreté résultent significativement (ce qui ne veut pas dire exclusivement) des systèmes de valeurs culturelles partagés au sein des groupes. Les facteurs par lesquels transite l'élément culturel vers le domaine de la pauvreté sont de deux ordres. Il s'agit du différentiel des perceptions et des déterminants de la pauvreté. Compte tenu de ces résultats et pour une stratégie crédible de réduction de la pauvreté, nous proposons une approche participative et décentralisée prudente pour définir les actions de lutte répondant aux besoins exprimés par les populations concernées.Pauvreté multidimensionnelle, culture, différentiel de pauvreté, MES, modèles nichés, variables latentes, indicateurs de pauvreté