Parametric and nonparametric bootstrap: an analysis of indoor air data from Kuwait
Keywords:
Coverage proportion, cross-validation, nonparametric bootstrap, parametric bootstrap, PM2.5.Abstract
This paper discusses the performance of parametric and nonparametric bootstrap for confidence interval (CI) estimation applied to fine particulate matter (PM2.5) data. Preceding the estimation process, several models were investigated to predict PM2.5 concentrations from various tobacco smoking venues that resulted in a weighted logarithmic regression (WLS) model as a best fit. This model is then used as the base fit throughout the bootstrap estimation of the total number of burned cigarettes within an hour for a given a specific air quality level.
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