%0 Journal Article
%A Moineddin, Rahim
%A Meaney, Christopher
%A Kalia, Sumeet
%T Finite Sample Properties of Quantile Interrupted Time Series Analysis: A Simulation Study
%J Journal of the Iranian Statistical Society
%V 20
%N 1
%U http://jirss.irstat.ir/article-1-784-fa.html
%R 10.52547/jirss.20.1.247
%D 2021
%K Interrupted Time-Series, Segmented Linear Regression, Segmented Quanti-le Regression, Monte Carlo Simulation Study.,
%X Interrupted Time Series (ITS) analysis represents a powerful quasi-experime-ntal design in which a discontinuity is enforced at a specific intervention point in a time series, and separate regression functions are fitted before and after the intervention point. Segmented linear/quantile regression can be used in ITS designs to isolate intervention effects by estimating the sudden/level change (change in intercept) and/or the gradual change (change in slope). To our knowledge, the finite-sample properties of quantile segmented regression for detecting level and gradual change remains unaddressed. In this study, we compared the performance of segmented quantile regression and segmented linear regression using a Monte Carlo simulation study where the error distributions were: IID Gaussian, heteroscedastic IID Gaussian, correlated AR(1), and T (with 1, 2 and 3 degrees of freedom, respectively). We also compared segmented quantile regresison and segmented linear regression when applied to a real dataset, employing an ITS design to estimate intervention effects on daily-mean patient prescription volumes. Both the simulation study and applied example illustrate the usefulness of quantile segmented regression as a complementary statistical methodolo-gy for assessing the impacts of interventions in ITS designs.
%> http://jirss.irstat.ir/article-1-784-fa.pdf
%P 247-267
%& 247
%!
%9 Special Issue, Original Paper
%L A-11-369-35
%+
%G eng
%@ 1726-4057
%[ 2021