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Joint regression modeling for missing categorical covariates in generalized linear models.
- Source :
-
Journal of Applied Statistics . 2018, Vol. 45 Issue 15, p2741-2759. 19p. 1 Diagram, 6 Charts, 1 Graph. - Publication Year :
- 2018
-
Abstract
- Missing covariates data is a common issue in generalized linear models (GLMs). A model-based procedure arising from properly specifying joint models for both the partially observed covariates and the corresponding missing indicator variables represents a sound and flexible methodology, which lends itself to maximum likelihood estimation as the likelihood function is available in computable form. In this paper, a novel model-based methodology is proposed for the regression analysis of GLMs when the partially observed covariates are categorical. Pair-copula constructions are used as graphical tools in order to facilitate the specification of the high-dimensional probability distributions of the underlying missingness components. The model parameters are estimated by maximizing the weighted log-likelihood function by using an EM algorithm. In order to compare the performance of the proposed methodology with other well-established approaches, which include complete-cases and multiple imputation, several simulation experiments of Binomial, Poisson and Normal regressions are carried out under both missing at random and non-missing at random mechanisms scenarios. The methods are illustrated by modeling data from a stage III melanoma clinical trial. The results show that the methodology is rather robust and flexible, representing a competitive alternative to traditional techniques. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02664763
- Volume :
- 45
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Journal of Applied Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 132187391
- Full Text :
- https://doi.org/10.1080/02664763.2018.1438376