On Nonregularized Estimation of Psychological Networks.

ABSTRACT: 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 descri

SUBMITTER: Williams DR 

PROVIDER: S-EPMC6736701 | biostudies-literature | 2019 Sep-Oct

REPOSITORIES: biostudies-literature