Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis.

ABSTRACT: We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. Although the hurdle model assumes the counts come from a mixture of a degenerate distribution at zero and a zero-truncated Poisson distribution, the ZIP model considers a mixture of a degenerate distribution at zero and a standard Poisson distribution. We extend the generalized functional linear model framework with a functional predictor and multiple cross-sectional predictors to model counts generated by a mixture distribution. We propose an estimation procedure for functional hurdle and ZIP models, called penalized reconstruction, geared towards error-prone and sparsely observed longitudinal functional predictors. The approach relies o

SUBMITTER: Senturk D 

PROVIDER: S-EPMC4221481 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature