Folks, I'm kind of newbie in R, but with some background in Matlab and VBA programming. Last month I was implementing a Maximum Likelihood Estimation in Matlab, but the algorithms didn't converge. So my academic advisor suggested using R. My problem is: estimate a mean reverting jump diffusion parameters. I've succeeded in deriving the likelihood function (which looks like a gaussian mixture) and it is implemented in R. My main doubts are related to the inputs and outputs that this function should generate, for instance, in Matlab this function should get the parameters as input and output the likelihood using the sample data (imported within the function). In order to make R optimizers to work I, apparently, should write a function that uses the parameters and the sample data as input and outputs the likelihood. Is it correct? Could someone reply with an example code which examplifies the type of function I should write and the syntax to optimize? Alternatively, could anyone suggest a good MLE tutorial and package?
Thankfully, JC -- View this message in context: http://n4.nabble.com/MLE-optimization-tp998655p998655.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
