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Related Experiment Videos

Simple bootstrap statistical inference using the SAS system.

S R Cole1

  • 1Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA. scole@rics.bwh.harvard.edu

Computer Methods and Programs in Biomedicine
|August 3, 1999
PubMed
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Nonparametric bootstrap statistical inference offers a robust computational approach for estimating statistical variability when standard formulas or assumptions are inadequate. This method is implemented via a versatile SAS macro, applicable to various procedures and clustered data scenarios.

Area of Science:

  • Statistics
  • Computational Statistics
  • Biostatistics

Background:

  • Traditional statistical inference often relies on known formulas or asymptotic assumptions.
  • These assumptions may not hold true for complex datasets or novel analytical methods.
  • Estimating statistical variability is crucial for hypothesis testing and confidence interval construction.

Purpose of the Study:

  • To present a SAS macro for implementing nonparametric bootstrap statistical inference.
  • To provide a practical example of its application.
  • To demonstrate the macro's generalizability for various statistical procedures and data structures.

Main Methods:

  • Nonparametric bootstrap resampling technique.
  • Implementation using a SAS macro.

Related Experiment Videos

  • Application to SAS procedures with BY statements and clustered data.

    • Main Results:

    • The developed SAS macro successfully performs nonparametric bootstrap inference.
    • The code is adaptable to diverse SAS procedures, including those with BY statements.
    • The method effectively handles clustered data, enhancing its applicability.

    Conclusions:

    • Nonparametric bootstrap is a valuable tool for statistical inference when standard methods fail.
    • The provided SAS macro offers a user-friendly and flexible implementation.
    • This approach expands the possibilities for robust statistical analysis in various research fields.