Literature DB >> 10430465

Simple bootstrap statistical inference using the SAS system.

S R Cole1.   

Abstract

Nonparametric bootstrap statistical inference is a robust computer intensive method for generating estimates of statistical variability for which formulae are not known or asymptotic assumptions are not met. A SAS macro that implements simple nonparametric bootstrap statistical inference is presented with an example. The program code is easily generalized to any SAS procedure which includes a BY statement, and to cases of clustered data.

Mesh：

Year:  1999        PMID： 10430465     DOI： 10.1016/s0169-2607(99)00016-4

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN： 0169-2607            Impact factor:   5.428


  6 in total

1.  Making do with what we have: use your bootstraps.

Authors:  Guillaume Calmettes; Gordon B Drummond; Sarah L Vowler
Journal:  Br J Pharmacol       Date:  2012-09       Impact factor: 8.739

2.  Making do with what we have: use your bootstraps.

Authors:  Guillaume Calmettes; Gordon B Drummond; Sarah L Vowler
Journal:  J Physiol       Date:  2012-08-01       Impact factor: 5.182

3.  Antibiotic treatment of diarrhoea is associated with decreased time to the next diarrhoea episode among young children in Vellore, India.

Authors:  Elizabeth T Rogawski; Daniel J Westreich; Sylvia Becker-Dreps; Linda S Adair; Robert S Sandler; Rajiv Sarkar; Deepthi Kattula; Honorine D Ward; Steven R Meshnick; Gagandeep Kang
Journal:  Int J Epidemiol       Date:  2015-04-29       Impact factor: 7.196

4.  Reduction in diarrhoeal rates through interventions that prevent unnecessary antibiotic exposure early in life in an observational birth cohort.

Authors:  Elizabeth T Rogawski; Steven R Meshnick; Sylvia Becker-Dreps; Linda S Adair; Robert S Sandler; Rajiv Sarkar; Deepthi Kattula; Honorine D Ward; Gagandeep Kang; Daniel J Westreich
Journal:  J Epidemiol Community Health       Date:  2015-11-30       Impact factor: 3.710

5.  Brief Report: Estimating Differences and Ratios in Median Times to Event.

Authors:  Elizabeth T Rogawski; Daniel J Westreich; Gagandeep Kang; Honorine D Ward; Stephen R Cole
Journal:  Epidemiology       Date:  2016-11       Impact factor: 4.822

6.  Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry.

Authors:  Eva Pagano; Alessio Petrelli; Roberta Picariello; Franco Merletti; Roberto Gnavi; Graziella Bruno
Journal:  BMC Health Serv Res       Date:  2015-12-30       Impact factor: 2.655
  6 in total

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