جلد 20، شماره 1 - ( 3-1400 )                   جلد 20 شماره 1 صفحات 247-267 | برگشت به فهرست نسخه ها


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Moineddin R, Meaney C, Kalia S. Finite Sample Properties of Quantile Interrupted Time Series Analysis: A Simulation Study. JIRSS. 2021; 20 (1) :247-267
URL: http://jirss.irstat.ir/article-1-784-fa.html
Finite Sample Properties of Quantile Interrupted Time Series Analysis: A Simulation Study. پژوهشنامه انجمن آمار ایران. 1400; 20 (1) :247-267

URL: http://jirss.irstat.ir/article-1-784-fa.html


چکیده:   (242 مشاهده)

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.

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نوع مطالعه: Original Paper | موضوع مقاله: 62Mxx: Inference from stochastic processes
دریافت: 1399/8/9 | پذیرش: 1399/11/22 | انتشار: 1400/3/30

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