Roberto Casarin, Antonio Peruzzi
arXiv 4 Nov 2024 · Statistics — Methodology
arXiv:2411.02531 · PDF · DOI · OpenAlex · Extracted main text
The techniques suggested in Fr\"uhwirth-Schnatter et al. (2024) concern sparsity and factor selection and have enormous potential beyond standard factor analysis applications. We show how these techniques can be applied to Latent Space (LS) models for network data. These models suffer from well-known identification issues of the latent factors due to likelihood invariance to factor translation, reflection, and rotation (see Hoff et al., 2002). A set of observables can be instrumental in identifying the latent factors via auxiliary equations (see Liu et al., 2021). These, in turn, share many analogies with the equations used in factor modeling, and we argue that the factor loading restrictions may be beneficial for achieving identification.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Frühwirth-Schnatter, Hosszejni \ Lopes (2024) `Sparse Bayesian Factor Analysis when the Number of Factors is Unknown', Bayesian Analysis 1(1), 1–31 | 0.405 | 1 | 1 | 100% |
| 2 | Hoff, Raftery \ Handcock (2002) `Latent Space Approaches to Social Network Analysis', Journal of the American Statistical Association 97(460), 1090–1098 | 0.405 | 1 | 1 | 100% |
| 3 | Liu, Jin, Zhang \ Yuan (2021) `Social Network Mediation Analysis: A Latent Space Approach', Psychometrika 86, 272–298 | 0.405 | 1 | 1 | 100% |
Showing the top 3 of 3 scored citations.