Thanks both for your replies. I tried the FMM models but got really weird predictions. Rick in an earlier post helped me with this. He pointed out the following in the Details tab of the PROC HPFMM help information under the sub-tab "Log likelihood of the response distributions": "While it is syntactically valid to mix a constant distribution with a continuous distribution, such as DIST=LOGNORMAL, such a mixture is not mathematically appropriate, because the constant log-likelihood is the log of a probability, while a continuous log-likelihood is the log of a probability density function. If you want to mix a constant distribution with a continuous distribution, you could model the constant as a very narrow continuous distribution, such as DIST=UNIFORM(c-delta,c+delta ) for a small value . However, using PROC HPFMM to analyze such mixtures is sensitive to numerical inaccuracy and ultimately unnecessary. Instead, the following approach is mathematically equivalent and more numerically stable: Estimate the mixing probability as the proportion of observations in the data set such that |y_i - c|< epsilon. Estimate the parameters of the continuous distribution from the observations for which |y_i - c|>=epsilon. " Sorry the equations won't copy. The link is http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_hpfmm_details07.htm I wasn't sure how to code this suggestion. Using the uniform as they suggested didn't work either. My interpretation of FMM models is that they are 2 part models,one process for getting a zero and another for getting a positive outcome, which is not what is happening here. Anyway I was really just wanting someone to say OLS with PROC HPREG is completely wrong(which is what I think but it seems to work),or you could use that but.... Regards Chris
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