Dr Lu Yang

CLA Statistics, School of
College of Liberal Arts
Twin Cities
Project Title: 
Assessment of Regression Models With Noncontinuous Outcomes

Regression models with noncontinuous (e.g. count, binary, ordinal, and semicontinuous) outcomes have been long and widely used in many domains of science and engineering. Having fit a regression model, judging the adequacy of the model’s fit is a routine and critical task in statistics. However, there is a lack of valid tools for assessment of regression models with noncontinuous outcomes.

The long-term goal of this research is to establish a principled framework for assessing regression models with noncontinuous outcomes. The envisioned framework includes informal graphical assessment tools and formal goodness-of-fit tests. Specifically, this project has two objectives:

  • Develop a goodness-of-fit test for regression models with general discrete outcomes in order to assist analysts to obtain p-values and draw conclusions on the adequacy of their models with statistical confidence. The theoretical properties of the proposed goodness-of-fit statistic will be studied using empirical process theory, and extensive simulation studies will be conducted to evaluate its rate of type I and type II errors.
  • Develop a graphical assessment tool and a goodness-of-fit test for regression models with semicontinuous outcomes, which are characterized with a point mass of observations at zero and the remaining observations following a positive-valued distribution.

Project Investigators

Dr Lu Yang
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