Nonparametric estimation of a latent variable model
提出一种两步法非参数估计潜变量模型：先估计因子分析模型，再用非参数回归分析潜变量关系，无需指定分布，并给出估计量的一致性理论结果。
In this paper a nonparametric latent variable model is estimated without specifying the underlying distributions. The main idea is to estimate in a first step a common factor analysis model under the assumption that each manifest variable is influenced by at most one of the latent variables. In a second step nonparametric regression is used to analyze the relation between the latent variables. Theoretical results concerning consistency of the estimates are presented, and the finite sample size performance of the estimates is illustrated by applying them to simulated data.