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From |
"Stas Kolenikov" <[email protected]> |

To |
[email protected] |

Subject |
Re: st: median regressin and survey data! |

Date |
Sat, 29 Mar 2008 11:15:56 -0500 |

Short answer: neither one fits well enough into the paradigm of survey sampling, so coming up with fully justifiable implementation is not straightforward. Long answer. For the first one, all rank tests implicitly assume the data are i.i.d., and I don't think very clear analogies are possible with survey data. There are no estimating equations to work with; you probably would be able to get the distribution of the test statistic over repeated sampling, but it won't be nearly as nice as the textbook distribution. For the second one, -qreg-is a heavily model-based concept: that for any combination of explanatory variables, there's a well defined distribution of responses over which the median can be computed. The straight design perspective, on the other hand, says that there are only so many individuals in the finite population, so there is no talk about conditional distributions. So one needs to invent some sort of a hybrid framework to incorporate both model and design ideas, and they don't always go hand in hand. A basic introduction to the subject is a chapter by Binder and Roberts in 2003 Analysis of Survey Data book (http://doi.wiley.com/10.1002/0470867205.ch3) -- I say introductory because they consider the simplest possible situations, but they still operate with big-O small-O in probability. Conceptually, it should still be possible to formulate median regression for sample surveys, as it is linked to a minimization problem, and thus can be cast in terms of estimating equations. Then you need to say, "If I had the full population, I would run this same median regression on it, and get some numbers from this census estimation procedure. Now, what I can hope for with the sample is that my estimates are going to be consistent for those numbers that came out of the census problem". I don't really know if that was done for quantile regression; for linear regression, the comparable result goes back to mid 1970s due to Wayne Fuller, and for generalized linear models, to David Binder's 1983 paper. Median regression is somewhat trickier though, as the function being minimzed, the sum of absolute deviations, is not differentiable, so the standard tools like the delta method are not applicable. On 3/29/08, Mohammed El Faramawi <[email protected]> wrote: > Hi, > I am trying to run non-parametric tests using survey > data ( probability weighted). unfortunately I can not > find commands which takes into the consideration the > pweight. I am interested in qreg (median regression) > and Mann-Whitney test. Is there any way to do this by > Stata? Thank you > Mohammed Faramawi, MD,Phd,MPH,Msc > > > ____________________________________________________________________________________ > OMG, Sweet deal for Yahoo! users/friends:Get A Month of Blockbuster Total Access, No Cost. W00t > http://tc.deals.yahoo.com/tc/blockbuster/text2.com > * > * 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/ > -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: Please do not reply to my Gmail address as I don't check it regularly. * * 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: median regressin and survey data!***From:*"Austin Nichols" <[email protected]>

**References**:**st: median regressin and survey data!***From:*Mohammed El Faramawi <[email protected]>

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