科研之友
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摘要
The missing data problem is common in longitudinal or hierarchical structure studies. In this paper, we propose a correlated random-effects model to fit normal longitudinal or cluster data when the missingness mechanism is nonignorable. Computational challenges arise in the model fitting due to intractable numerical integrations. We obtain the estimates of the parameters based on an accurate approximation of the log likelihood, which has higher-order accuracy but with less computational burden than the existing approximation. We apply the proposed method to a real data set arising from an autism study.
- 出版日期2010-1-30
- 单位四川大学
