The number of latent factors, in factor analysis, is typically unknown and motivated by a rich literature on priors distributions, which progressively penalize the number of factors in infinite factor models. Adaptive Gibbs samplers that truncate the infinite factor models are typically used for posterior inference. In this paper, we introduce a novel strategy to adaptively truncate the number of factors that is more interpretable, stable and consistent, with respect to standard approaches.
On the truncation criteria in infinite factor models
Lorenzo Schiavon;
2020-01-01
Abstract
The number of latent factors, in factor analysis, is typically unknown and motivated by a rich literature on priors distributions, which progressively penalize the number of factors in infinite factor models. Adaptive Gibbs samplers that truncate the infinite factor models are typically used for posterior inference. In this paper, we introduce a novel strategy to adaptively truncate the number of factors that is more interpretable, stable and consistent, with respect to standard approaches.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
