xEM1: EM Algorithm for General State Space Models - This script has...

In astsa: Applied Statistical Time Series Analysis

EM Algorithm for General State Space Models - This script has been superseded by EM.

Description

Estimation of the parameters in the general state space model via the EM algorithm. Inputs are not allowed; see the note. NOTE: This script has been superseded by EM and scripts starting with an x are scheduled to be phased out.

Usage

xEM1(num, y, A, mu0, Sigma0, Phi, cQ, cR, max.iter = 100, tol = 0.001)

Arguments

num	number of observations
y	observation vector or time series; use 0 for missing values
A	observation matrices, an array with dim=c(q,p,n); use 0 for missing values
mu0	initial state mean
Sigma0	initial state covariance matrix
Phi	state transition matrix
cQ	Cholesky-like decomposition of state error covariance matrix Q – see details below
cR	R is diagonal here, so cR = sqrt(R) – also, 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NOTE: This script has been superseded by EM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.