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The framework also handles missing values through data imputation.
Single-variable methods estimate missing values through available measurements of the same environmental variable (e.g., NO2, CO or O3).
We will handle missing values through multiple imputation [ 53].
Consequently, we estimated missing values through multiple imputation by chained equations using Stata's user written program ice[ 4] (STATA ® 9.2, Stata Corporation, College Station, Texas, USA).
Advantages of this method include not only making fewer modeling assumptions, but also establishing procedure that adapts to missing values through the use of surrogate measures [ 4].
We estimated the missing values through a multiple imputation (MI) method, in which the distribution of the observed data is used to estimate a set of plausible values for the missing observations [ 11].
Similar(53)
Then the k-nearest observations that have non-missing values for that particular variable are used to impute a missing value through a weighted mean of the neighbouring values.
This demonstrated that the imputed data was consistent with the measured data across all tests and the missing values derived through imputation were valid.
Incomplete data sets are frequently analyzed using multiple imputation, which involves creating multiple complete versions of the data with missing values imputed through random draws from distributions inferred from observed data (2).
Missing values were replaced through linear interpolation.
However, missing values can arise through several mechanisms.
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