Re: [R] (no subject)

From: Berton Gunter <>
Date: Wed 07 Sep 2005 - 03:41:21 EST

> -----Original Message-----
> From:
> [] On Behalf Of Nadja Riedwyl
> Sent: Tuesday, September 06, 2005 10:22 AM
> To:
> Subject: [R] (no subject)
> my problem actually arised with fitting the data to the
> weibulldistribution,
> where it is hard to see, if the proposed parameterestimates
> make sense.
> data1:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;145
> 42;694;11491;
> 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334
> how am I supposed to know what starting values i have to take?
> i get different parameterestimates depending on the starting
> values i choose,
> this shouldn't be, no? how am i supposed to know, which the
> "right" estimates
> should be?

This is a general issue with all (gradient-based) optimization methods when the response to be optimized has many local optima and/or is poorly conditioned. As Doug Bates and others have often remarked, finding good starting values is an "art" that is often problem-specific. Ditto for "good" parameterizations. There is no universal "magic" answer.

In many respects, this is the monster hiding in the closet of many of the complex modeling methods being proposed in statistics and other disciplines: when the response function to be optimized is a nonlinear function of "many" parameters, convergence may be difficult to achieve. Presumably stochastic optimization methods like simulated annealing and mcmc are less susceptible to such problems, but they pay a large efficiency price to be so.

Cheers, mailing list PLEASE do read the posting guide! Received on Wed Sep 07 03:44:16 2005

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