Re: OT Quantile Regression Why not ranks or percentiles?

Posted by Art Kendall on Oct 16, 2013; 7:02pm
URL: http://spssx-discussion.165.s1.nabble.com/OT-Quantile-Regression-Why-not-ranks-or-percentiles-tp5722584p5722586.html

That makes sense, the interest would be in n slope coefficients so too many such slopes would be very difficult to interpret.
On 10/16/2013 1:59 PM, Jon K Peck wrote:

Quantile regression builds models of the specified quantiles in the same way as ordinary regression builds models of means, except that they are computationally much more complex.  I doubt that anyone would want to build models of, say, every quantile in (0,1) by .1.  If you are interested in how the coefficients vary by quantile, a half dozen or so points should give a pretty good picture.


Jon Peck (no "h") aka Kim
Senior Software Engineer, IBM
[hidden email]
phone: [phone]




From:        Art Kendall [hidden email]
To:        [hidden email],
Date:        10/16/2013 11:48 AM
Subject:        [SPSSX-L] OT  Quantile Regression  Why not ranks or percentiles?
Sent by:        "SPSSX(r) Discussion" [hidden email]

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Just curious.

As a general rule of thumb one wants a variable to be as fine grained as is practical in the situation.
However, the few examples I have seen of quantile regression have coarsened to 5 or so values.

Is there a substantive or computational reason for  using this few values?
--
Art Kendall
Social Research Consultants

Art Kendall
Social Research Consultants

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