Hi Ravi, And remember that the vanilla rounding procedure is biased upward. That is, an observation of 5 actually may have ranged from 4.5 to 5.4.
Jim On Thu, Oct 22, 2015 at 7:15 AM, peter salzman <peter.salzmanu...@gmail.com> wrote: > here is one thought: > > if you plug in your numbers into any kind of regression you will get > prediction that are real numbers and not necessarily integers, it may be > that you predictions are good enough with this approximate value of Y. you > could test this by randomly shuffling your data by +- 0.5 and compare the > results with the original result. > > let me add another idea: > > if data is not fully observed this falls under the umbrella of censored > data, in this case you have interval censoring. if you see 5 then the > observations is in interval [4.5, 5.5] > i'm not familiar with the field but i'd search for 'regression with > interval censoring' > > > peter > > > On Wed, Oct 21, 2015 at 10:53 AM, Ravi Varadhan <ravi.varad...@jhu.edu> > wrote: > > > Hi, > > I am dealing with a regression problem where the response variable, time > > (second) to walk 15 ft, is rounded to the nearest integer. I do not care > > for the regression coefficients per se, but my main interest is in > getting > > the prediction equation for walking speed, given the predictors (age, > > height, sex, etc.), where the predictions will be real numbers, and not > > integers. The hope is that these predictions should provide unbiased > > estimates of the "unrounded" walking speed. These sounds like a > measurement > > error problem, where the measurement error is due to rounding and hence > > would be uniformly distributed (-0.5, 0.5). > > > > Are there any canonical approaches for handling this type of a problem? > > What is wrong with just doing the standard linear regression? > > > > I googled and saw that this question was asked by someone else in a > > stackexchange post, but it was unanswered. Any suggestions? > > > > Thank you, > > Ravi > > > > Ravi Varadhan, Ph.D. (Biostatistics), Ph.D. (Environmental Engg) > > Associate Professor, Department of Oncology > > Division of Biostatistics & Bionformatics > > Sidney Kimmel Comprehensive Cancer Center > > Johns Hopkins University > > 550 N. Broadway, Suite 1111-E > > Baltimore, MD 21205 > > 410-502-2619 > > > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > 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. > > > > > > -- > Peter Salzman, PhD > Department of Biostatistics and Computational Biology > University of Rochester > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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 -- To UNSUBSCRIBE and more, see 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.