Search Results - subject:"Missing covariates"

Hierarchical likelihood methods for nonlinear and generalized linear mixed models with missing data and measurement errors in covariates

Noh, Maengseok; Wu, Lang; Lee, Youngjo - In: Journal of Multivariate Analysis 109 (2012) C, pp. 42-51

Nonlinear mixed-effects (NLME) models and generalized linear mixed models (GLMM) are popular in the analyses of longitudinal data and clustered data. Covariates are often introduced to partially explain the large between individual (cluster) variation. Many of these covariates, however, contain...

Persistent link: https://www.econbiz.de/10010572298

A generalized missing-indicator approach to regression with imputed covariates

Dardanoni, Valentino; Luca, Giuseppe De; Modica, Salvatore - In: Stata Journal 12 (2012) 4, pp. 575-604

We consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the miss- ing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only...

Persistent link: https://www.econbiz.de/10010630743

No Customer Left Behind: A Distribution-Free Bayesian Approach to Accounting for Missing Xs in Marketing Models

Qian, Yi; Xie, Hui - In: Marketing Science 30 (2011) 4, pp. 717-736

-free approach, which can ensure that no customer is left behind in the analysis as a result of missing covariates. In this way, all …, the proposed approach reduces modeling and computational efforts associated with missing covariates and therefore makes … simulation studies. We then illustrate the method in two real data examples in which missing covariates occur: a mixed …

Persistent link: https://www.econbiz.de/10010990395

Semiparametric Maximum Likelihood for Missing Covariates in Parametric Regression

Zhang, Zhiwei; Rockette, Howard - In: Annals of the Institute of Statistical Mathematics 58 (2006) 4, pp. 687-706

Persistent link: https://www.econbiz.de/10005395828