Add to ORKGWe derive explicit formulae for estimation in logistic regression models where some of the covariates are missing. Our approach allows for modeling the distribution of the missing covariates either as a multivariate normal or multivariate t-distribution. A main advantage of this method is that it is fast and does not require the use of iterative procedures. A model selection method is derived which allows to choose amongst these distributions. In addition we consider versions of AIC that are based on the EM algorithm and on multiple imputation methods that have a wide applicability to model selection in likelihood models in general
In clinical settings, missing data in the covariates occur frequently. For example, some markers are...
We present an EM based solution to missing categorical covariates in Binomial models with logit link...
Application of classical model selection methods such as Akaike’s information cri-terion AIC becomes...
We derive explicit formulae for estimation in logistic regression models where some of the covariate...
We derive explicit formulae for estimation in logistic regression models where some of the covariate...
We derive explicit formulae for estimation in logistic regression models where some of the covariate...
This research deals with logistic regression models under the pres-ence of missing covariates on som...
International audienceLogistic regression is a common classification method in supervised learning. ...
[[abstract]]This article considers semiparametric estimation in logistic regression with missing cov...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Missing covariate data often arise in various settings, including surveys, clinical trials, epidemio...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Logistic regression is one of the most important tools in the analysis of epidemiological and clinic...
In this article, we propose and explore a multivariate logistic regression model for analyzing multi...
AbstractIn this article, we propose and explore a multivariate logistic regression model for analyzi...
In clinical settings, missing data in the covariates occur frequently. For example, some markers are...
We present an EM based solution to missing categorical covariates in Binomial models with logit link...
Application of classical model selection methods such as Akaike’s information cri-terion AIC becomes...
We derive explicit formulae for estimation in logistic regression models where some of the covariate...
We derive explicit formulae for estimation in logistic regression models where some of the covariate...
We derive explicit formulae for estimation in logistic regression models where some of the covariate...
This research deals with logistic regression models under the pres-ence of missing covariates on som...
International audienceLogistic regression is a common classification method in supervised learning. ...
[[abstract]]This article considers semiparametric estimation in logistic regression with missing cov...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Missing covariate data often arise in various settings, including surveys, clinical trials, epidemio...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Logistic regression is one of the most important tools in the analysis of epidemiological and clinic...
In this article, we propose and explore a multivariate logistic regression model for analyzing multi...
AbstractIn this article, we propose and explore a multivariate logistic regression model for analyzi...
In clinical settings, missing data in the covariates occur frequently. For example, some markers are...
We present an EM based solution to missing categorical covariates in Binomial models with logit link...
Application of classical model selection methods such as Akaike’s information cri-terion AIC becomes...