We present a novel strategy of statistical inference for graphical models with latent Gaussian variables, and observed variables that follow non-standard sampling distributions. We restrict our attention to those graphs in which the latent variables have a substantive interpretation. In addition, we adopt the assumption that the distribution of the observed variables may be meaningfully interpreted as arising after marginalising over the latent variables. We illustrate the method with two studies that investigate developmental changes in cognitive functions of young children in one case and of cognitive decline of Alzheimer’s patients in the other. These studies involve the assessment of competing causal models for several psychological constructs; and the observed measurements are gathered from the administration of batteries of tasks subject to complicated sampling protocols.
Graphical models with latent variables and their application in developmental psychology
Ivonne Solís, MRC Human Nutrition Research
Thu, 28/03/2013 - 16:30