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Tomoshige Nakamura

Keio University



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Mihoko Minami

Keio University



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123 – SPEED: Environmental Statistics

Underestimation of Standard Errors in Regression Analysis for Pollution Exposure Assessment Using Multi-source Data

Sponsor: Section on Statistics and the Environment
Keywords: Regression imputation method, Particulate matter, Land Use Regression

Tomoshige Nakamura

Keio University

Mihoko Minami

Keio University

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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