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From |
Roger Harbord <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Using nonlinear constraints in an ordered probit |

Date |
Wed, 20 Jan 2010 22:09:40 +0000 |

Possibly isotonic regression, as estimated using the 'pool-adjacent-violators algorithm (PAVA), might be one solution. Wim van Putten wrote a program -ira- to do isotonic regression analysis using PAVA as part of a package -wvpreg- that used to be available from his personal website, but unfortunately that no longer exists. -ira- was written in Stata version 5.0 so appears a little old-fashioned now but still works. The package gives Wim van Putten's email address as [email protected] -- a quick check on Google Scholar suggests he's still at Erasmus University Medical Center, so anyone wishing to obtain the package could try emailing him. Roger. -- Roger Harbord http://www.epi.bris.ac.uk/staff/rharbord.htm On Wed, Jan 20, 2010 at 8:49 PM, <[email protected]> wrote: > Would not the poster's particular problem be solved by a version of > the "pool-adjacent-violators" algorithm?. See: > http://fedc.wiwi.hu-berlin.de/xplore/ebooks/html/anr/anrhtmlnode43.html > > Steve > > On Wed, Jan 20, 2010 at 3:13 PM, Maarten buis <[email protected]> wrote: >> --- On Wed, 20/1/10, Dana Chandler wrote: >>> Does anyone else have a possible >>> answer? I don't think the below >>> response has answered this question. >> >> You may not like the answer, but it did >> answer your questions. The problem is that >> unless someone has already implemented that >> in a program and submitted it to SSC, any >> respons will be similar to the Stata FAQ >> you already refered to. You can't expect an >> answer that is more elaborate (or even as >> elaborate) as that FAQ. >> >> -- Maarten >> >> >>> >>> Although I appreciate the book reference and confirmation >>> that the >>> article I mentioned is applicable to oprobit or any other >>> model, the >>> below post has not provided any guidance on how to >>> proceed. >>> >>> Have any other researchers solved this specific problem, >>> since I don't >>> think it is that unusual of a problem. >>> >>> Thanks in advance, >>> Dana >>> >>> >>> >>> On Wed, Jan 20, 2010 at 2:36 AM, Maarten buis <[email protected]> >>> wrote: >>> > --- On Tue, 19/1/10, Dana Chandler wrote: >>> >> oprobit y pop_1 pop_2 pop_3 pop_4 >>> >> >>> >> ... and I would like to constrain each successive >>> >> population parameter has a coefficient lower than >>> the >>> >> previous so that (pop_2-pop_1)>=0, >>> (pop_3-pop_2)>=0, etc. >>> >> >>> >> >>> >> I recognize that there has been a thread (and faq) >>> from a >>> >> few years back (<http://www.stata.com/support/faqs/stat/ >>> > intconst.html>) explaining how to set up interval >>> (non-linear) >>> >> constraints using ML to perform a linear >>> regression. However, >>> >> even though the article suggests that I could use >>> the similar >>> >> methodology to derive it for probits, I'm not 100% >>> sure that >>> >> it's directly applicable in my case and for >>> oprobits. >>> > >>> > It is also applicable to oprobit, or any other model. >>> > >>> >> I have not worked with stata's ML function before. >>> Does >>> >> anyone have any advice? >>> > >>> > If you are serious about getting into this type of >>> modeling then >>> > you can't go wrong by getting: William Gould, Jeffrey >>> Pitblado, >>> > William Sribney (2006) Maximum Likelihood Estimation >>> with Stata. >>> > College Station: Stata Press. >>> > http://www.stata.com/bookstore/mle.html >>> > >>> > Hope this helps, >>> > Maarten >>> > * * 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/

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