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RE: st: AW: ksmirnov


From   "Helge Molde" <Helge.Molde@psysp.uib.no>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: AW: ksmirnov
Date   Wed, 30 Sep 2009 08:53:20 +0200

Ok! Når da?

h 

>-----Original Message-----
>From: owner-statalist@hsphsun2.harvard.edu 
>[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
>tzygmund mcfarlane
>Sent: Tuesday, September 29, 2009 8:49 PM
>To: statalist@hsphsun2.harvard.edu
>Subject: Re: st: AW: ksmirnov
>
>Thank you Nick! This is precisely what I needed.
>
>Now if I can figure out how to do kernel densities with nonintegral
>degrees of freedom t-distributions overlaid...
>
>T
>
>On Tue, Sep 29, 2009 at 7:18 PM, Nick Cox <n.j.cox@durham.ac.uk> wrote:
>> 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?
>>
>>
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>>
>
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