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From |
Hyeok Jeong <hjeong@usc.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: RE: Re: Imposing Parameter Restriction |

Date |
Fri, 06 Aug 2004 15:17:30 -0700 |

Dear Joseph and Hung-Jen, Thank you for the kind advice, which I tried. Basically the idea of reparametrization of Hung-Jen worked. And the idea of converting nl to ml of Joseph will be tried also. Thanks! Hyeok -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Joseph Coveney Sent: Thursday, August 05, 2004 6:35 PM To: Statalist Subject: st: Re: Imposing Parameter Restriction Hyeok Jeong wrote: I am trying to estimate a nonlinear model using "nl" and this is my first time of doing it. Let's say the model is written as "y= f(l;a,b)," where a and b are the parameters to be estimated. The model restricts the parameter space such that "0 < a < 1" and "b <1". Without imposing any restrictions, it turns out that estimates from "nl" method violate these restrictions. I am just guessing there should be some obvious ways of imposing restrictions on parameter space, which I cannot find. Could anyone help me on how I can put these restrictions on parameter space, using "nl" (nonlinear least square) or using any nonlinear estimation method in stata in general? ---------------------------------------------------------------------------- ---- To my knowledge there is no simple way to impose parameter space boundaries in -nl- in Stata. I ran into the same problem some years ago with using -nl- for pharmacokinetic parameter estimation, which have a positive parameter space. One approach is to reparameterize the coefficient as ln(coefficient), which assures that the estimate will lie above zero. But there is no easy boundary statement in -nl- to my knowledge. If it's any consolation, in my experience years ago with specialized pharmacokinetics packages and with SAS's PROC NONLIN, which do allow simple boundary statements, the badly behaving estimates often slam up against the boundary in the first iteration or two and then sit there at the boundary throughout the remaining iterations until "convergence," unless a fortuitous local saddlepoint was found in the sum squares. You might wish to consider writing an -ml- implementation of your nonlinear regression. It's actually not that difficult, and it provides a good alternative to least squares. To see how, refer to the FAQ on StataCorp's Web site, "How do I estimate a nonlinear model using ml?" http://www.stata.com/support/faqs/stat/nl_ml.html . -ml- will allow the -constraint- option, and it might be possible to define a set of constraints that will hold the coefficients in the parameter space. Joseph Coveney * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Re: Imposing Parameter Restriction***From:*Joseph Coveney <jcoveney@bigplanet.com>

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