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Activity Number: 200
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 11:15 AM
Sponsor: Section on Statistics and the Environment
Abstract #321740
Title: Underestimation of Standard Errors in Regression Analysis for Pollution Exposure Assessment Using Multi-Source Data
Author(s): Tomoshige Nakamura* and Mihoko Minami
Companies: Keio University and Keio University
Keywords: regression imputation ; generalized linear model ; estimating equation ; particulate matter ; community health survey ; land use regression
Abstract:

We are interested in investigating the effect of particulate matter exposure on human heath in Japan using community health survey data. However, in Japan, the number of the monitoring stations around the survey area is very limited and the observations on these measurements at local community area are not generally available. When particulate matter concentrations are not observed in survey area, Land Use Regression (LUR) is often used to fill the missing values.

In general, if we use regression imputation to fill the missing values, the inference based on regression imputed data might be wrong. For example, the consistency of estimator may be violated, and the variance of estimator may be underestimated. So, in our research, we try to clarify the problem using regression imputation when we estimate the effect of particulate matter.


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