ZIM Regression under Complex Sampling Designs, Application in Hospital Inpatient Charges Data.

Music City Center 

An underlying population may contain a large proportion of zero values, which causes the population distribution to spike at zero, and such a population is referred to as a zero-inflated population. Zero-inflated populations can be seen in many applications and such populations are analyzed via a two-component mixture model. I will present some examples of zero-inflated populations and explain the estimation problem in generalized linear regression models. I will describe the zero-inflated mixture (ZIM) regression model under complex probability sampling designs via two-component mixture models where the probability distribution of non-zero components is supposed to be parametric. The maximum pseudo-likelihood procedure is proposed to estimate the expected responses at "future" covariate values/vectors. The simulation results show that under some complex probability sampling designs, new confidence intervals based on the pseudo-likelihood function perform significantly better than the standard/classic procedures. The proposed new procedure is applied to hospital data about inpatient charges in dollars.

Zero-Inflated

Sampling Designs

Regression

Simulation

Hospital Inpatient Charges (in Dollars) 

Section on Statistical Consulting