Semiparametric Methods with Mixed Measurement Error and Misclassication in Covariates

Friday, April 26, 2019
11:00AM, Fretwell 122

Hosted by Dr. Yanqing Sun, UNC Charlotte

Measurement error arises ubuquitously from various fields including health sciences, epidemiological stidoes, survey research, sconomics, and so on. It has been a longstanding concern in data analysis and has attracted extensive research interest over the past few decades. The effects of measurement error are complex and vary from problem to problem. While there are settings where measurement errror effects are negligible, it has been well documented that ignoring measurement error in statistical analyses often yields erroneous or even misleading results. It is sensible to conduct a case-by-case examination in order to reach a valid statistical analysis for error-contaminated data. Although in practice both measurement errorin covariates and misclassification in covariates may occur simultaneously, researcg attention in the literature has mainly focused on addressing either one of these problems separately but not both. In this talk, I will discuss issues pertinent to analysis of error-contaminated data and describe several methods of handling data with both measurement error and misclassification in covariates.