Analysing interrupted time series with a control

DOI10.1515/em-2018-0010zbMath1445.62220OpenAlexW2939390336MaRDI QIDQ2192294

J. Anthony G. Scott, Christian Bottomley, Valerie S. Isham

Publication date: 16 August 2020

Published in: Epidemiologic Methods (Search for Journal in Brave)

Full work available at URL: https://discovery.ucl.ac.uk/id/eprint/10066791/3/Isham_%5BEpidemiologic%20Methods%5D%20Analysing%20Interrupted%20Time%20Series%20with%20a%20Control.pdf

zbMATH Keywords

segmented regressioncommon trend modelinterrupted time series

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Stopping times; optimal stopping problems; gambling theory (60G40)

Uses Software

• R
• Stata

Cites Work

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• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
• Intervention Analysis with Applications to Economic and Environmental Problems
• How Much Should We Trust Differences-In-Differences Estimates?
• All of Nonparametric Statistics

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