Nonparametric estimation for longitudinal data with informative missingness
Nonparametric estimation for longitudinal data with informative missingness
Longitudinal data analysis is of great interest in a wide array of disciplines across the medical, economic, and social sciences. In this chapter, the authors propose a new non-parametric estimating equation (NEE) approach to estimation based on longitudinal data subjected to informative missing mechanisms. The NEE approach provides a method for exploring informative non-response in the longitudinal setting, which is computationally easy and flexible in specification. Moreover, the plug-in observed NEE will be somewhat biased if the ‘score-term’ in the population EE is correlated with the response propensity, as in the case of informative non-response. The matter is considered, including possible venues for bias adjustment. The associated variance estimation is described. The authors illustrate and investigate the performance of the NEE approach using a simulation study.
491-512
Ahmad, Zahoor
1a4418f6-8855-4244-a89f-882eadff9721
Zhang, Li Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
22 March 2021
Ahmad, Zahoor
1a4418f6-8855-4244-a89f-882eadff9721
Zhang, Li Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Ahmad, Zahoor and Zhang, Li Chun
(2021)
Nonparametric estimation for longitudinal data with informative missingness.
In,
Lynn, Peter
(ed.)
Advances in Longitudinal Survey Methodology.
(Wiley Series in Probability and Statistics)
Wiley, .
(doi:10.1002/9781119376965.ch20).
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Book Section
Abstract
Longitudinal data analysis is of great interest in a wide array of disciplines across the medical, economic, and social sciences. In this chapter, the authors propose a new non-parametric estimating equation (NEE) approach to estimation based on longitudinal data subjected to informative missing mechanisms. The NEE approach provides a method for exploring informative non-response in the longitudinal setting, which is computationally easy and flexible in specification. Moreover, the plug-in observed NEE will be somewhat biased if the ‘score-term’ in the population EE is correlated with the response propensity, as in the case of informative non-response. The matter is considered, including possible venues for bias adjustment. The associated variance estimation is described. The authors illustrate and investigate the performance of the NEE approach using a simulation study.
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Published date: 22 March 2021
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Local EPrints ID: 477386
URI: http://eprints.soton.ac.uk/id/eprint/477386
PURE UUID: 483f8a35-3c2c-4a52-b287-34a935bc424e
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Date deposited: 05 Jun 2023 16:58
Last modified: 17 Mar 2024 03:30
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Author:
Zahoor Ahmad
Editor:
Peter Lynn
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