Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany.

ABSTRACT: In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logisti

SUBMITTER: Wang Z 

PROVIDER: S-EPMC5525141 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature