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From |
"Nick Cox" <[email protected]> |

To |
<[email protected]> |

Subject |
st: RE: goodness of fit for t-distribution |

Date |
Thu, 9 Sep 2004 16:30:12 +0100 |

I doubt very much that these calculations have been previously programmed anywhere using Stata. In practice, people don't often seem to fit t distributions to _data_, even though a common refrain is that real data are commonly fatter-tailed than the Gaussian. A good reason for that would be that real data are often skewed too. Be that as it may, one answer is: consider a graph rather than a test. Let's suppose that you think that your data look like some t distribution with some particular df. How you got that df I don't know. But knowing the df is necessary, unless you have some smart way of estimating it from the data or some smart theory that tells you what the df should be. (Some education theory tells us that Students should be allowed to behave with infinite degrees of freedom, but is that a normal expectation?) [Warning: some wordplay there.] By far the best tool is then going to be a dedicated plot that lets you look at e.g. the quantiles of a t distribution and your data. Official Stata has -qnorm-, -qchi-, ... and stops there. But all quantile-quantile plots are the same, really. So you can make a -qt- just by hacking at e.g. -qnorm-. Here's mine. I didn't bother with implementing a -grid-. *! 1.0.0 NJC 9 Sept 2004 program qt, sort version 8 syntax varname [if] [in] , df(numlist int >0) [ * ] _get_gropts , graphopts(`options') getallowed(rlopts plot) local options `"`s(graphopts)'"' local rlopts `"`s(rlopts)'"' local plot `"`s(plot)'"' _check4gropts rlopts, opt(`rlopts') tempvar touse Z Psubi quietly { gen byte `touse' = !missing(`varlist') `if' `in' sort `varlist' gen float `Psubi' = sum(`touse') replace `Psubi' = cond(`touse'==.,.,`Psubi'/(`Psubi'[_N]+1)) sum `varlist' if `touse'==1, detail gen float `Z' = invttail(`df', 1 - `Psubi') *r(sd) + r(mean) label var `Z' "Inverse t, `df' df" local xttl : var label `Z' local fmt : format `varlist' format `fmt' `Z' } local yttl : var label `varlist' if `"`yttl'"' == "" { local yttl `varlist' } if `"`plot'"' == "" { local legend legend(nodraw) } version 8: graph twoway /// (scatter `varlist' `Z', /// sort /// ytitle(`"`yttl'"') /// xtitle(`"`xttl'"') /// `legend' /// ylabels(, nogrid) /// xlabels(, nogrid) /// `yl' /// `xl' /// note(`"`note'"') /// `options' /// ) /// (function y=x, /// range(`Z') /// n(2) /// clstyle(refline) /// yvarlabel("Reference") /// yvarformat(`fmt') /// `rlopts' /// ) /// || `plot' /// // blank end Then you can (1) qt myvar, df(#) (2) derive a portfolio of plots to get an idea of what samples from genuine t distributions should look like using -rndt- from the Hilbe and friend -rnd- package: sysuse auto forval i = 1/20 { qui rndt 74 10 qt xt, df(10) t1(simulation `i') more } There is, admittedly, still the option of using some classical goodness-of-fit test like chi-square or Kolmogorov-Smirnov, but you need to do most of the calculations yourself, as you fear. Nick [email protected] Michael Stobernack > if I need a test for normality (for one variable) I use > sktest, swilk or sfrancia. But what if I need a test for > t-distribution? > There is a pull down menue: > Statistics/Summaries, tables, & tests/Nonparametric tests of > hypotheses/ > One-sample Kolmogorov-Smirnov test/Expression > > But I don't know what to put in the expression field. > Is there any ado-file to run a goodness of fit test for t-distribution > without knowing the formula for the cdf of t-distribution? > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: goodness of fit for t-distribution***From:*Michael Stobernack <[email protected]>

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