Paper 16 Modeling local dependence in latent vector autoregressive models

(Tran et al. 2019)

16.1 英文摘要简介

We propose a [Bayesian latent vector autoregressive(LVAR)] model to analyze [multivariate longitudinal] data of [binary and ordinal variables (items)] as a function of [a small number of continuous latent variables].

We focus on the [evolution of the latent variables] while taking into account [the correlation structure of the responses].

[Often local independence] is assumed in this context. Local independence implies that, given the latent variables, the responses are assumed mmutually independent cross-sectionally and longitudinally.

但是在实践中latent variable作为条件可能不能移除responses的相关性。 in practice conditioning on the latent variables may not remove the dependence of the response.

We address local dependency by further conditioning on item-specific random effects.

A simulation study shows that wrongly assuming local independence may give biased estimates for the regression coefficients of the LVAR process as well as the item-specific parameters.

Novel features of our proposal include 1) correcting有偏估计的模型参数,特别是LVAR process的回归系数， 2) measuring the magnitude of local dependence. 测量局部相关性的量级。

We applied our model on data obtained from []. The purpose was to examine the [values] of [oral health information] on top of [general health information].

Varius applications in medicine, sociology, psychology, etc. require analyzing multivariate longitudinal data. In these applications, a set of subjects is repeatedly measured over time and the subject’s condition is expressed by a number of correlated variables(items).

References

Tran, Trung Dung, Emmanuel Lesaffre, Greet Verbeke, and Joke Duyck. 2019. “Modeling local dependence in latent vector autoregressive models.” Biostatistics.