Inference for longitudinal data with nonignorable nonmonotone missing responses

Sanjoy K. Sinha, Amit Kaushal and Wenzhong Xiao

Computational Statistics & Data Analysis, 2014, vol. 72, issue C, 77-91

Abstract: For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.

Keywords: False discovery rate; Importance sampling; Incomplete data; Linear mixed model; Longitudinal study; Maximum likelihood; Proteomics experiment (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:72:y:2014:i:c:p:77-91

DOI: 10.1016/j.csda.2013.10.027

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