```Date: Tue, 1 Jan 2008 04:59:28 -0800 Reply-To: JianAn Lu Sender: "SAS(r) Discussion" From: JianAn Lu Organization: http://groups.google.com Subject: Re: Regression where dependent variable has an upperbound Comments: To: sas-l@uga.edu Content-Type: text/plain; charset=ISO-8859-1 On Jan 1, 5:10 am, liuwen...@GMAIL.COM (Wensui Liu) wrote: > Mary, > for the simplest case, what i learned from stat101 is that > yhat = y|x = x'b, where y = yhat + e and e ~ normal() such that E(e) = 0 > > however, per your sas code in the email, the my takehome message is that > y = yhat + max(e, 0) > which i don't know how to solve it mathematically. > > just my \$0.02. > > On Dec 31, 2007 3:11 PM, Mary wrote: > > > > > > > Xu Zeng, > > > Is it really necessary to do this within the logistic regression procedure? Why couldn't you save the predicted values, merge them to the actual values, and then in a data step adjust any values that are over the owed amount to the owed amount, like this? > > > data new; > > /* merge {predicted and actual } */ > > > if predicted_owed_amount > actual_owed_amount then > > do; > > predicted_owed_amount=actual_owed_amount; > > end; > > > -Mary > > ----- Original Message ----- > > From: Xu Zeng > > To: SA...@LISTSERV.UGA.EDU > > Sent: Friday, December 28, 2007 9:10 AM > > Subject: Regression where dependent variable has an upperbound > > > I am trying to predict dollar amount collected for people who have various > > debts. But this dependent variable can never exceed the original amount of > > debt people owe. For example, if someone owes \$5000 then the predicted > > collected \$ must be <=5000 (I am not considering interests for simplicity). > > > I will definitely use the original owed amount as one of independent > > variabls and the coefficient of it will be less than 1 (in a linear > > regression). But I will probably have many other variables, I can't 100% > > sure that the predicted amount will be less than original debt amount. Of > > course, I can adjust the prediction at the end, but I would like to know if > > there is a SAS regression procedure or statistical method that will > > guarantee the prediction never exceeds the upperbound. > > > Your advice and suggestion will be greatly appreciated. > > > Thank you very much and happy new year. > > -- > =============================== > WenSui Liu > Statistical Project Manager > ChoicePoint Precision Marketing > (http://spaces.msn.com/statcompute/blog) > ===============================- Hide quoted text - > > - Show quoted text - How about regression on the odds of expected lost: log(expected lost/ (bad debt-expected lost))? ```

Back to: Top of message | Previous page | Main SAS-L page