Making Sense of Noisy Data with Measurement Error Or/And Missing Observations (ADDED FEE) — Professional Development Continuing Education Course
ASA
Technological advances associated with data acquisition are leading to the production of larger and more complex data sets. The increases in dimension and structural complexity have led to an urgent need for the development of novel and flexible modeling tools to facilitate rigorous and efficient analysis. A very important concern on analyzing such data is the quality and provenance of the data. Typically, the challenges presented by noisy data with measurement error and missing observations are particularly intriguing, and such data arise ubiquitously from various fields including health sciences,
epidemiological studies, survey research, economics, and so on.
Effects of measurement error or missing observations have been a long standing concern in data analysis and research on data with such features has attracted extensive attention over the past few decades. It has been well documented that ignoring measurement error or missing data in statistical analyses may lead to erroneous or even misleading results. The effects of measurement error or missing data are, however, complex and affected by various factors.
The objective of this course is to lead the audience to visit these challenging but exciting areas. Specifically, the impact of measurement error and missing data will be demonstrated and different types of measurement error models and missing data mechanisms will be discussed. Typical inference strategies for handling measurement error and missing data will be described. The discussion will be illustrated with examples and applications.
The prerequisite for taking this course is the basic statistics knowledge such as the likelihood method. Anticipated audience include graduate students, researchers, and analysts who are interested in having an overview of measurement error and missing data.
The course materials are partly based on a newly published monograph, "Yi, G. Y. (2017). Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application. Springer Science+Business Media LLC, New York."
