Semiparametric model for regression analysis with nonmonotone missing data

Yang Zhao ()
Additional contact information
Yang Zhao: University of Regina

Statistical Methods & Applications, 2021, vol. 30, issue 2, No 4, 475 pages

Abstract: Abstract Semiparametric likelihoods for regression models with missing at random data (Chen in J Am Stat Assoc 99:1176–1189, 2004, Zhang and Rockette in J Stat Comput Simul 77(2):163–173, 2007, Zhao et al. in Biom J 51: 123–136, 2009, Zhao in Commun Stat Theory Methods 38:3736–3744, 2009) are robust as they use nonparametric models for covariate distributions and do not require modeling the missing data probabilities. Furthermore, the EM algorithms based on the semiparametric likelihoods have closed form expressions for both E-step and M-step. As far as we know the semiparametric likelihoods can only deal with the simple monotone missing data pattern. In this research we extend the semiparemetric likelihood approach to deal with regression models with arbitrary nonmonotone missing at random data. We propose a pseudo-likelihood model, which uses an empirical distribution to model the conditional distribution of missing covariates given observed covariates for each missing data pattern separately. We show that an EM algorithm with closed form updating formulas can be used for computing maximum pseudo-likelihood estimates for regression models with nonmonotone missing data. We then propose estimating the asymptotic variance of the maximum pseudo-likelihood estimator through a profile log likelihood and the EM algorithm. We examine the finite sample performance of the new methods in simulation studies and further illustrate the methods in a real data example investigating high risk gambling behavior and the associated factors.

Keywords: EM algorithm; Nonmonotone missing data patterns; Profile log likelihood; Pseudo-likelihood; Semiparametric likelihood (search for similar items in EconPapers)
Date: 2021
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/s10260-020-00530-w 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:stmapp:v:30:y:2021:i:2:d:10.1007_s10260-020-00530-w

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-020-00530-w

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().