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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: AW: ksmirnov |

Date |
Tue, 29 Sep 2009 19:18:25 +0100 |

I don't know why I said that because it isn't true. In fact, I was overlooking my own work... In terms of the original example, webuse wpi1 g returns = D.ln_wpi and given a download of -qplot- from the Stata Journal site, you can go qplot returns, trscale(invttail(6, 1 - @)) xli(0) yli(0) Alternatively, you can do it from first principles count if !missing(returns) local N = r(N) sort returns gen quantilet = invttail(6, 1 - (_n - 0.5) / `N') scatter returns quantilet , xli(0) yli(0) For (_n - 0.5) / `N', substitute any other plotting position formula. For "6" substitute any other desired df. In this example, the fit is lousy: the mean is a long way from zero and the distribution is not even symmetric. See also for a meant-to-be-encouraging note SJ-7-2 gr0027 . . Stata tip 47: Quantile-quantile plots without programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox Q2/07 SJ 7(2):275--279 (no commands) tip on producing various quantile-quantile (Q-Q) plots That and 75 other tips have just been reprinted in Seventy-six Stata Tips, 2nd Edition Publisher: Stata Press Copyright: 2009 ISBN-10: 1-59718-071-8 ISBN-13: 978-1-59718-071-9 Pages: 177; paperback Price: $29.00 <http://www.stata.com/bookstore/tips2.html> Nick n.j.cox@durham.ac.uk Nick Cox ======== You'd need to clone one or more existing programs, e.g. -qnorm-, -pnorm- and replace code there with code specific to the t-distribution. I am not familiar with the etiquette on fitting t-distributions. Isn't the df in effect a parameter to be estimated? Otherwise, there would need to be some justification for using a particular df. tzygmund mcfarlane ================== Thanks for your replies Martin & Nick. Martin: My question was actually simpler - was my procedure correct? That is, should the data be standardised by an estimate of the scale before using the Kolmogorov-Smirnov procedure or is that not necessary? Also, from the help file for chi2fit by Stas Kolenikov I could not figure out how to implement it for a t-distribution. Any help will be appreciated. Nick: I am particularly interested in deviation from a t-distribution. My data is almost certainly non-normal. I agree about the merits of plotting it, but am not aware of any tools for my particular case. Any ideas? * * 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/

**Follow-Ups**:**Re: st: AW: ksmirnov***From:*tzygmund mcfarlane <tzygmund@googlemail.com>

**References**:**Re: st: AW: ksmirnov***From:*tzygmund mcfarlane <tzygmund@googlemail.com>

**RE: st: AW: ksmirnov***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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