EM0 {astsa} | R Documentation |

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

EM0(num, y, A, mu0, Sigma0, Phi, cQ, cR, max.iter = 50, tol = 0.01)

`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 |

`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).

`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 |

D.S. Stoffer

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]