ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue on Causal Discovery and Inference: Volume 7 Issue 2, January 2016
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Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable ...
missing not at random, outcome-dependent missing, Causal inference, instrumental variable, noncompliance, principal stratification
Computational Statistics & Data Analysis: Volume 84 Issue C, April 2015
Publisher: Elsevier Science Publishers B. V.
Latent class models with crossed subject-specific and test(rater)-specific random effects have been proposed to estimate the diagnostic accuracy (sensitivity and specificity) of a group of binary tests or binary ratings. However, the computation of these models are hindered by their complicated Monte Carlo Expectation-Maximization (MCEM) algorithm. In this article, a ...
Composite likelihood, Latent class models, Sensitivity and specificity, Imperfect reference standards, Random effects
Journal of Multivariate Analysis: Volume 117, May, 2013
Publisher: Academic Press, Inc.
Life history data arising in clusters with pre-specified assessment time points for patients often feature incomplete data since patients may choose to visit the clinic based on their needs. Markov process models provide a useful tool describing disease progression for life history data. The literature mainly focuses on time homogeneous ...
62N02, Transition intensity, Cluster, 62F10, Markov non-homogeneous, Random effects, 62H12, Missing not at random