title: A Newton procedure for conditionally linear mixed-effects models.
abstract: This article reviews Newton procedures for the analysis of mean and covariance structures that may be functions of parameters that enter a model nonlinearly. The kind of model considered is a mixed-effects model that is conditionally linear with regard to its parameters. This means parameters entering the model nonlinearly must be fixed, whereas those entering linearly may vary across individuals. This framework encompasses several models, including hierarchical linear models, linear and nonlinear factor analysis models, and nonlinear latent curve models. A full maximum-likelihood estimation procedure is described. Mx, a statistical software package often used to estimate structural equation models, is considered for estimation of these models. An example with Mx syntax is provided.
authors.affiliation: Psychology Department, University of California, Davis, California 95616, USA. sablozis@ucdavis.edu
authors.forename: Shelley A
authors.initials: SA
authors.lastname: Blozis
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D017711:Nonlinear Dynamics; D011601:Psychophysics / Q000706:statistics & numerical data*