xEM0: EM Algorithm for Time Invariant State Space Models - This...

In astsa: Applied Statistical Time Series Analysis

EM Algorithm for Time Invariant State Space Models - This script has been superseded by EM.

Description

Estimation of the parameters in a simple state space via the EM algorithm. NOTE: This script has been superseded by EM. Note that scripts starting with an x are scheduled to be phased out.

Usage

xEM0(num, y, A, mu0, Sigma0, Phi, cQ, cR, max.iter = 50, tol = 0.01)

Arguments

num	number of observations
y	observation vector or time series
A	time-invariant observation matrix
mu0	initial state mean vector
Sigma0	initial state covariance matrix
Phi	state transition matrix
cQ	Cholesky-like decomposition of state error covariance matrix Q – see details below
cR	Cholesky-like decomposition of state error covariance matrix R – see details below
max.iter	maximum number of iterations
tol	relative tolerance for determining convergence

Details

cQ and cR are the Cholesky-type decompositions of Q and R. In particular, Q = t(cQ)%*%cQ and R = t(cR)%*%cR is all that is required (assuming Q and R are valid covariance matrices).

Value

Phi	Estimate of Phi
Q	Estimate of Q
R	Estimate of R
mu0	Estimate of initial state mean
Sigma0	Estimate of initial state covariance matrix
like	-log likelihood at each iteration
niter	number of iterations to convergence
cvg	relative tolerance at convergence

Note

NOTE: This script has been superseded by EM

Author(s)

D.S. Stoffer

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

astsa documentation built on May 29, 2024, 10:29 a.m.