Just as a follow-up on T. Lumley's post, 2 citations that may be useful in reference to application of quantile regression with survey samples are:
Bassett and Saleh. 1994. L_1 estimation of the median of a survey population. Nonparametric Statistics 3: 277-283. (L_1 estimation of median is 0.50 quantile regression). Bassett et al. 2002. Quantile models and estimators for data analysis. Metrika 55: 17-26. (describes weighted QR for survey of school characteristics and student achievement scores). Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: [EMAIL PROTECTED] tel: 970 226-9326 "Stas Kolenikov" <[EMAIL PROTECTED]> Sent by: [EMAIL PROTECTED] 08/20/2008 01:14 PM To "Cheng, Yiling (CDC/CCHP/NCCDPHP)" <[EMAIL PROTECTED]> cc r-help@r-project.org Subject Re: [R] Quantile regression with complex survey data On Wed, Aug 20, 2008 at 8:12 AM, Cheng, Yiling (CDC/CCHP/NCCDPHP) <[EMAIL PROTECTED]> wrote: > I am working on the NHANES survey data, and want to apply quantile > regression on these complex survey data. Does anyone know how to do > this? There are no references in technical literature (thinking, Annals, JASA, JRSS B, Survey Methodology). Absolutely none. Zero. You might be able to apply the procedure mechanically and then adjust the standard errors, but God only knows what the population equivalent is of whatever that model estimates. If there is a population analogue at all. In general, a quantile regression is a heavily model based concept: for each value of the explanatory variables, there is a well defined distribution of the response, and quantile regression puts additional structure on it -- linearity of quantiles wrt to some explanatory variables. That does not mesh well with the design paradigm according to which the survey estimation is usually conducted. With the latter, the finite population and characteristics of every unit are assumed fixed, and randomness comes only from the sampling procedure. Within that paradigm, you can define the marginal distribution of the response (or any other) variable, but the conditional distributions may simply be unavailable because there are no units in the population satisfying the conditions. -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
