ABSTRACT
This chapter continues discussing the estimation of pairwise Markov random fields—undirected network models in which edges indicate the strength of conditional associations—introduced in Chapter 6. While Chapter 6 was concerned with the interpretation and saturated estimation (i.e., network structures estimated with all edges included) of such models, this chapter is concerned with unsaturated estimation and model search strategies: how to select which edges should be included in the network model. The chapter discusses four methods of estimating the model structure: thresholding (removing edges that do not meet some criterion), pruning (thresholding followed by re-estimation of non-zero edge-weights), extensive model search strategies (searching through the model space for an optimal model), and finally regularization (penalized likelihood estimation resulting in a sparse model). The chapter ends with recommendations for which estimation strategy should be used in which setting.
