EM0 {astsa}R Documentation

EM Algorithm for Time Invariant State Space Models

Description

Estimation of the parameters in a simple state space via the EM algorithm.

Usage

EM0(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

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 are https://www.stat.pitt.edu/stoffer/tsa4/ and https://www.stat.pitt.edu/stoffer/tsda/.


[Package astsa version 1.14 Index]