Dear R-helpers, Anybody knows which function can I use to comupute maximum likelihood standard errors? Using the function "nlm" I can get the estimate of the parameters of any likelihood that I want (for example now I am working on a jump diffusion process) but what about the standard error? Is there a function that I can use to calculate the second derivate of the likelihood function respect to the vector of parameters? Thank you so much for your help, Carlo Fezzi
maximum likelihood standard deviation
3 messages · Carlo Fezzi, Dimitris Rizopoulos, Ben Bolker
look at mle() (package "stats4") and optim() (i.e., "optim(..., hessian = TRUE)"). I hope it helps. Best, Dimitris ---- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/16/336899 Fax: +32/16/337015 Web: http://www.med.kuleuven.ac.be/biostat/ http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm ----- Original Message ----- From: "Carlo Fezzi" <fezzi at stat.unibo.it> To: <r-help at stat.math.ethz.ch> Sent: Thursday, June 02, 2005 1:43 PM Subject: [R] maximum likelihood standard deviation
Dear R-helpers, Anybody knows which function can I use to comupute maximum likelihood standard errors? Using the function "nlm" I can get the estimate of the parameters of any likelihood that I want (for example now I am working on a jump diffusion process) but what about the standard error? Is there a function that I can use to calculate the second derivate of the likelihood function respect to the vector of parameters? Thank you so much for your help, Carlo Fezzi
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Carlo Fezzi <fezzi <at> stat.unibo.it> writes:
Dear R-helpers, Anybody knows which function can I use to comupute maximum likelihood standard errors? Using the function "nlm" I can get the estimate of the parameters of any likelihood that I want (for example now I am working on a jump diffusion process) but what about the standard error? Is there a function that I can use to calculate the second derivate of the likelihood function respect to the vector of parameters? Thank you so much for your help, Carlo Fezzi
______________________________________________ R-help <at> stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
if you set hessian=TRUE when calling nlm it will give you exactly what you want -- second derivative [sic] of the likelihood w.r.t. parameters (provided of course that your objective function in nlm() is the negative log-likelihood). solve() on the hessian should invert the Hessian and give you the variance-covariance matrix (then sqrt(diag()) to get the s.d.s)