Maximum softly penalised likelihood in factor analysis

原文摘要

Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, known as Heywood cases, are characterised by non-positive variance estimates and can cause issues in numerical optimisation procedures or convergence failures, which, in turn, can lead to misleading inferences, particularly regarding factor scores and model selection. We derive sufficient conditions on the model and a penalty to the log-likelihood function that i) guarantee the existence of maximum penalised likelihood estimates in the interior of the parameter space, and ii) ensure that the corresponding estimators possess the desirable asymptotic properties expected by the maximum likelihood estimator, namely consistency and asymptotic normality. Consistency and asymptotic normality are achieved when the penalisation is soft enough, in a way that adapts to the information accumulation about the model parameters. We formally show, for the first time, that the penalties of Akaike (1987) and Hirose et al. (2011) to the log-likelihood of the normal linear factor model satisfy the conditions for existence, and, hence, deal with Heywood cases. Their vanilla versions, though, can result in questionable finite-sample properties in estimation, inference, and model selection. The maximum softly-penalised likelihood framework we introduce enables the careful scaling of those penalties to ensure that the resulting estimation and inference procedures inherit the ML estimator's optimal properties. Through comprehensive simulation studies and the analysis of real data sets, we illustrate the desirable finite-sample properties of the maximum softly penalised likelihood estimators and associated procedures.

摘要来源：arXiv