Library Header Image
LSE Research Online LSE Library Services

Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions

Vitoratou, Silia, Ntzoufras, Ioannis and Moustaki, Irini (2016) Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions. Statistics and Computing, 26 (1). pp. 333-348. ISSN 0960-3174

PDF - Accepted Version
Download (440kB) | Preview

Identification Number: 10.1007/s11222-014-9495-8


In latent variable models parameter estimation can be implemented by using the joint or the marginal likelihood, based on independence or conditional independence assumptions. The same dilemma occurs within the Bayesian framework with respect to the estimation of the Bayesian marginal (or integrated) likelihood, which is the main tool for model comparison and averaging. In most cases, the Bayesian marginal likelihood is a high dimensional integral that cannot be computed analytically and a plethora of methods based on Monte Carlo integration (MCI) are used for its estimation. In this work, it is shown that the joint MCI approach makes subtle use of the properties of the adopted model, leading to increased error and bias in finite settings. The sources and the components of the error associated with estimators under the two approaches are identified here and provided in exact forms. Additionally, the effect of the sample covariation on the Monte Carlo estimators is examined. In particular, even under independence assumptions the sample covariance will be close to (but not exactly) zero which surprisingly has a severe effect on the estimated values and their variability. To address this problem, an index of the sample's divergence from independence is introduced as a multivariate extension of covariance. The implications addressed here are important in the majority of practical problems appearing in Bayesian inference of multi-parameter models with analogous structures.

Item Type: Article
Official URL:
Additional Information: © 2014 Springer Science+Business Media New York
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 14 Jul 2014 16:16
Last Modified: 20 Jul 2021 02:29

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics