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.
