Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: re: st: QREG Question |

Date |
Sat, 28 Jul 2012 12:58:54 -0400 |

I should have clarified that -sqreg- can be used to run the model simultaneously across the quantiles of your choice: sysuse auto sqreg weight foreign, quantile(.25 .5 .75) reps(100) Ariel -----Original Message----- From: Ariel Linden, DrPH [mailto:ariel.linden@gmail.com] Sent: Saturday, July 28, 2012 12:42 PM To: statalist@hsphsun2.harvard.edu Subject: re: st: QREG Question Hi Patrick, It seems to me that you have data that fits a Poisson-like distribution (the data should be examined for over-dispersion to indicate whether a negative binomial model or perhaps zero inflated models are better suited). As far as -qreg-, I suggest you read the help file, and even better, the manual, since qreg has been updated in version 12. The term "quantile" in the name quantile regression does not mean that it will give you results in quantiles, but it means that you can indicate which quantile value you are interested in assessing (ie., 0.5 is the default quantile which is the median, but you can plug in any value on the continuum from 0-1). Of course, you can run the model across different quantiles, so you are not limited to only one level. Another option (depending on you data and what you are trying to evaluate) is the -somersd- suite of non-parametric models, which was written by Roger Newson and comes with its own set of manuals (for somersd, cendif and censlope) - findit somersd-. I hope this helps Ariel Date: Fri, 27 Jul 2012 15:33:30 -0400 From: Arnold Behrer <behrer@post.harvard.edu> Subject: st: QREG Question Hello, I have two questions, one related to stats generally and the other to Stata specifically. The first is about how useful quantile regression is in correcting for skewed data. I have a data set heavily skewed to the right. I have considered log transforming it but I would rather not because the skewness is due in large part to zero values that are meaningful in the context of the regression. I thought that perhaps quantile regression might be a way of correcting for this skew without dropping the zero values. Is that true? The second question is simply whether the Qreg command in Stata runs a normal quantile regression - splitting the dependent into quantles? Cheers, Patrick Behrer * * 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/

- Prev by Date:
**re: st: QREG Question** - Next by Date:
**Re: st: QREG Question** - Previous by thread:
**re: st: QREG Question** - Next by thread:
**Re: st: QREG Question** - Index(es):