Generalized partial linear models with nonignorable dropouts

Yujing Shao and Lei Wang ()
Additional contact information
Yujing Shao: Nankai University
Lei Wang: Nankai University

Metrika: International Journal for Theoretical and Applied Statistics, 2022, vol. 85, issue 2, No 4, 223-252

Abstract: Abstract In the presence of longitudinal data with nonignorable dropouts, we propose improved estimators for generalized partial linear models that accommodate both the within-subject correlations and nonignorable missing data. To address the identifiability problem, an instrumental covariate, which is related to the response variable but unrelated to the propensity given the response variable and other covariates, is used to construct sufficient instrumental estimating equations. Subsequently, the nonparametric function is approximated by B-spline basis functions and then we construct bias-corrected generalized estimating equations based on the inverse probability weighting. In order to incorporate the within-subject correlations under an informative working correlation structure, we borrow the idea of quadratic inference function and hybrid-GEE to construct the improved empirical likelihood procedures. Under some regularity conditions, we establish asymptotic normality of the proposed estimators for the parametric components and convergence rate of the estimators for the nonparametric functions. The finite-sample performance of the proposed estimators is studied through simulations and an application to HIV-CD4 data set is also presented.

Keywords: Dropout propensity; Empirical likelihood; Inverse probability weighting; Missing not at random; Nonresponse instrument (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00184-021-00828-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:85:y:2022:i:2:d:10.1007_s00184-021-00828-z

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-021-00828-z

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().