Title: Some Quantile Regression Models for Zero-Inflated Continuous Data with Applications
Presenter: Khiria Mohamed
When? 4-5pm, Thurs. Nov. 24
Where? Dillon 354
We developed new models for quantile regression for zero-inflated, non-negative data defined on intervals of the form [0,a) where a=1 or ∞. The first host of models rely on two parametric distributions known as Generalized exponential and Kumaraswamy distributions, respectively, for data defined in [0, ∞) and [0,1). The second set of models are semi-parametric models in which the zeros are modeled through a logistic regression model and the positive part of the data are modeled by using the usual linear/non-linear quantile regression models. We perform intensive Monte Carlo simulations to assess the performance of all proposed methods and we illustrate the utility of the methodologies by applying them to data sets on vehicle corrosion.
Counts toward seminar attendance for MSc and PhD students in Math & Stats