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From |
Maarten buis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: RE: RE: RE: highly skewed, highly zeroed data |

Date |
Thu, 26 Nov 2009 03:29:29 -0800 (PST) |

--- On Thu, 26/11/09, Nick Cox wrote: > Although with lots of zeros and strong skew the distribution > concerned is awkward practically, I'd be surprised if it was > pathological mathematically, or indicative of an underlying > distribution that was. The point could be explored a little > by e.g. bootstrapping. Here is an attempt to do that. It looks at the computing the mean in a Chi square distribution with .1 degrees of freedom. It shows that the sampling distribution is better approximated by the normal distribution if the mean is computed in larger samples, which doesn't surprise me. This example requires the -hangroot- package, which is downloadable by typing in Stata: -ssc install hangroot- *--------------- begin example -------------------- // see the chi^2(.1) distribution twoway function y = gammaden(`=.1/2',2,0,x) // looks pretty skewed to me... // simulate the sampling distirbution set more off program drop _all program define sim, rclass drop _all set obs `1' gen y = rchi2(`2') sum y, meanonly return scalar m = r(mean) end simulate m=r(m), reps(10000) : sim 500 .1 hangroot m , susp ci theoropt(lpattern(-)) // sampling distribution looks a bit off simulate m=r(m), reps(10000) : sim 5000 .1 hangroot m , susp ci theoropt(lpattern(-)) // but in a larger sample it looks about fine *------------------ end example ----------------- ( For more on how to use examples I sent to statalist see: http://www.maartenbuis.nl/stata/exampleFAQ.html ) Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: RE: RE: RE: highly skewed, highly zeroed data***From:*"Nick Cox" <[email protected]>

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