作者：Qiu, Yuqi; Messer, Karen
作者单位：East China Normal University; University of California System; University of California San Diego
摘要：We develop a novel doubly-robust (DR) imputation framework for longitudinal studies with monotone dropout, motivated by the informative dropout that is common in FDA-regulated trials for Alzheimer's disease. In this approach the missing data are first imputed using a doubly-robust augmented inverse probability weighting (AIPW) estimator; then the imputed completed data are substituted into a full-data estimating equation, and the estimate is obtained using standard software. The imputed comple...
作者：Wee, Damien c. h.; Chen, Feng; Dunsmuir, William t. m.
作者单位：University of New South Wales Sydney
摘要：Maximum likelihood estimation of the famous GARCH(1, 1) model is generally straightforward, given the full observation series. However, when some observations are missing, the marginal likelihood of the observed data is intractable in most cases of interest, also intractable is the likelihood from temporally aggregated data. For both these problems, we propose to approximate the intractable likelihoods through sequential Monte Carlo (SMC). The SMC approximation is done in a smooth manner so th...
