Analysis of panel data under hidden mover-stayer models.

Abstract:

:Analysis of panel data is often challenged by the presence of heterogeneity and state misclassification. In this paper, we propose a hidden mover-stayer model to facilitate heterogeneity for a population that consists of two subpopulations each of movers or of stayers and to simultaneously account for state misclassification. We develop an inference procedure based on the expectation-maximization algorithm by treating the mover-stayer indicator and underlying true states as latent variables. We evaluate the performance of the proposed method and investigate the impact of ignoring misclassification through simulation studies. The proposed method is applied to analyze the data arising from the Waterloo Smoking Prevention Project. Copyright © 2017 John Wiley & Sons, Ltd.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Yi GY,He W,He F

doi

10.1002/sim.7346

subject

Has Abstract

pub_date

2017-09-10 00:00:00

pages

3231-3243

issue

20

eissn

0277-6715

issn

1097-0258

journal_volume

36

pub_type

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