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
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