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From |
Margaret MacDougall <Margaret.MacDougall@ed.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Bootstrap sampling for evaluating hypothesis tests |

Date |
Mon, 10 Jun 2013 16:44:51 +0100 |

Hello

Many thanks Best wishes Margaret ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Dr Margaret MacDougall Medical Statistician and Researcher in Education Centre for Population Health Sciences University of Edinburgh Medical School Teviot Place Edinburgh EH8 9AG Tel: +44 (0) 131 650 3211 Fax: +44 (0) 131 650 6909 E-mail: Margaret.MacDougall@ed.ac.uk http://www.chs.med.ed.ac.uk/cphs/people/staffProfile.php?profile=mmacdoug On 13/03/2013 15:45, Maarten Buis wrote:

On Wed, Mar 13, 2013 at 4:04 PM, Margaret MacDougall wrote:I would value receiving recommendations on literature explaining the application of bootstrap sampling to assess robustness to Type I errors of a proposed new hypothesis test. Better still, if the recommended references contain corresponding computer syntax!Rich Williams and I are currently working on such a project. In general I would not say that a test is "robust" against Type I errors but that the Type I error rate corresponds to your prespecified level of significance. Type I errors will occur, but the chance of it occuring should be the same as the level of significance you have chosen. This means that if we change the data such that the null hypothesis is true and bootstrap from that changed dataset the p-values should follow a uniform distribution. This changing the data is inevitable when assessing the Type I error rate: in order to assess the probability of rejecting a true null hypothesis you first need to make sure that the null hypothesis is true. Here are two examples of how to do this in Stata: *------------------ begin example ------------------ clear all sysuse auto recode rep78 1/2=3 logit foreign price predict double pr gen byte ysim = . keep foreign price rep78 pr ysim keep if !missing(foreign,price,rep78) program define sim replace ysim = runiform()< pr logit ysim price ib3.rep78 test 4.rep78 = 5.rep78 = 0 end simulate chi2=r(chi2) p=r(p), reps(1000) : sim simpplot p qchi chi2, df(2) name(q) *------------------- end example ------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) *------------------ begin example ------------------ clear all sysuse auto gen lnprice = ln(price) reg turn mpg i.rep78 foreign predict double mu1 reg turn mpg i.rep78 foreign weight lnprice predict double mu2 gen double ysim = turn - mu2 + mu1 keep ysim mpg rep78 foreign weight lnprice keep if !missing(ysim, lnprice, mpg, rep78, foreign, weight) tempfile temp save `temp' program define qenv_sim_F use `1', clear bsample reg ysim mpg i.rep78 foreign weight lnprice test weight lnprice end simulate F=r(F) p=r(p), reps(1000): qenv_sim_F `temp' simpplot p *------------------- end example ------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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