Computation of standard errors.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read Online Read More Add to Saved list
  • Author(s): Dowd BE; Greene WH; Norton EC
  • Source:
    Health services research [Health Serv Res] 2014 Apr; Vol. 49 (2), pp. 731-50.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Blackwell Country of Publication: United States NLM ID: 0053006 Publication Model: Print Cited Medium: Internet ISSN: 1475-6773 (Electronic) Linking ISSN: 00179124 NLM ISO Abbreviation: Health Serv Res Subsets: MEDLINE
    • Publication Information:
      Publication: Malden, MA : Blackwell
      Original Publication: Chicago, Hospital Research and Educational Trust.
    • Subject Terms:
      Statistics as Topic/*methods; Confidence Intervals ; Models, Statistical ; Statistics as Topic/standards
    • Abstract:
      Objectives: We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages.
      Study Design: We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects. EMPIRICAL APPLICATION: Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky–Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5.
      Conclusions: In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values.
    • Comments:
      Comment in: Health Serv Res. 2016 Jun;51(3):1117-9. (PMID: 27089857)
      Comment in: Health Serv Res. 2016 Jun;51(3):1114-6. (PMID: 27091682)
      Comment in: Health Serv Res. 2016 Jun;51(3):1109-13. (PMID: 27091770)
    • References:
      Health Serv Res. 2008 Jun;43(3):1102--20. (PMID: 18546544)
      Health Serv Res. 2012 Feb;47(1 Pt 1):255-74. (PMID: 22091735)
      J Am Stat Assoc. 1994 Dec;89(428):1,175-89. (PMID: 12155397)
      Health Econ. 2007 Aug;16(8):827-40. (PMID: 17238222)
      Biostatistics. 2005 Jan;6(1):93-109. (PMID: 15618530)
    • Publication Date:
      Date Created: 20140507 Date Completed: 20140519 Latest Revision: 20240322
    • Publication Date:
      20240322
    • Accession Number:
      PMC3976195
    • Accession Number:
      10.1111/1475-6773.12122
    • Accession Number:
      24800304