des our brief tutorial.
Hope this helps,
Ravi.
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
-
> Date: Fri, 30 Oct 2009 09:29:06 +0100
> From: Christophe Dutang
> Subject: Re: [R] [R-SIG-Finance] Fast optimizer
> To: R_help Help
> Cc: r-help@r-project.org
>> > Ok. I have the following likelihood function.
>> >
>> > L <- p*dpois(x,a)*dpois(y,b+c)+(1-p)*dpois(x,a+c)*dpois(y,b)
>> >
>> > whe
ogy
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: R_help Help
Date: Thursday, October 29, 2009 9:21 pm
Subject: Re: [R] Fast optimizer
To: Ravi Varadhan
Cc: r-help@r-project.org, r-sig-fina...@stat.math.ethz.ch
&
Ok. I have the following likelihood function.
L <- p*dpois(x,a)*dpois(y,b+c)+(1-p)*dpois(x,a+c)*dpois(y,b)
where I have 100 points of (x,y) and parameters c(a,b,c,p) to
estimate. Constraints are:
0 < p < 1
a,b,c > 0
c < a
c < b
I construct a loglikelihood function out of this. First ignoring th
Dear R_Help Help,
The critical questions are
a) how many parameters do you have
b) how pathological is the log-likelihood function
c) how good are your initial values and
d) how efficiently have you coded your objective function?
Of these, the last is most likely the critical one, and the one
You have hardly given us any information for us to be able to help you. Give
us more information on your problem, and, if possible, a minimal,
self-contained example of what you are trying to do.
Ravi.
Ravi Varadhan, Ph.D.
A
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