Selected article for: "segmented regression analysis and subset model"
Author: Noh, Jin-Won; Yoo, Ki-Bong; Kwon, Young Dae; Hong, Jin Hyuk; Lee, Yejin; Park, Kisoo
Title: Effect of Information Disclosure Policy on Control of Infectious Disease: MERS-CoV Outbreak in South Korea
  • Document date: 2020_1_1
    • ID: 1g3u7xno_12
    Snippet: Segmented linear autoregressive error models for interrupted time series were used to assess the effect of disclosure of hospital names and disease-related hospital management on the number of laboratory-confirmed MERS-CoV cases [11] . Segmented regression analysis is a useful statistical method for evaluating the longitudinal effect of policy intervention in quasi-experimental designs without a control group [12] . SAS 9.4 PROC AUTOREG was used .....
    Document: Segmented linear autoregressive error models for interrupted time series were used to assess the effect of disclosure of hospital names and disease-related hospital management on the number of laboratory-confirmed MERS-CoV cases [11] . Segmented regression analysis is a useful statistical method for evaluating the longitudinal effect of policy intervention in quasi-experimental designs without a control group [12] . SAS 9.4 PROC AUTOREG was used to create segmented linear autoregressive error models via SAS Version 9.4 (SAS Institute, Inc., Cary, NC, USA). The order of the autoregressive error models was selected using stepwise auto regression with BACKSTEP option in SAS 9.4. All parameters were estimated via maximum likelihood estimators. Daily numbers of quarantined individuals and laboratory-confirmed MERS-CoV cases were used as units of analysis. The equation for laboratory-confirmed MERS-CoV cases is shown below (Equation (1)). The stepwise autoregressive process selected a seventh-order subset model with nonzero parameters at lag 7, AR [7] error.

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