[R] State-space model estimation with EM

[Giovanni Petris]

Package "dlm" has a function for maximum likelihood estimation of
parameters in general (linear Normal) state space model. The function,
dlmMLE, computes the likelihood based on singular value decomposition
and appears to be fairly robust. 

No EM algorithm, though. 


Giovanni

> Date: Sun, 11 Nov 2007 18:32:32 +0000 (GMT)
> From: adschai at optonline.net
> Sender: r-help-bounces at r-project.org
> Priority: normal
> Precedence: list
> 
> Hi - I follow some references and now implement my own state-space model estimation. I have a question. In case, my equations are like this:
> 
> y(t) = Ax(t)+Bu(t)+eps(t) # observation eq
> x(t) = Cx(t-1)+Du(t)+eta(t) # state eq
> 
> Using EM, after backward recursion, you will use the smoothed state estimation to update the A, B, C, and D which is chosen so as to maximize the expectation equation. But for example, if my C is always of matrix zero (model specification), during EM I still get the value of estimated C which turns out to be non-zero. How can I resolve this conflict? Or I just ignore my estimation result and keep it as zero? THank you.
> 
> - adschai
> 
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-- 

Giovanni Petris  <GPetris at uark.edu>
Associate Professor
Department of Mathematical Sciences
University of Arkansas - Fayetteville, AR 72701
Ph: (479) 575-6324, 575-8630 (fax)
http://definetti.uark.edu/~gpetris/