asymptoticCov: Asymptotic covariance matrix of the HMM parameters

In RHmm: Hidden Markov Models Simulations and Estimations

Asymptotic covariance matrix of the HMM parameters

Description

This function calculates the empirical asymptotic covariance matrix of the HMM parameters

Usage

asymptoticCov(HMM, obs)

Arguments

HMM	A HMMClass or a HMMFitClass object
obs	The vector, matrix, data frame, list of vectors or list of matrices of observations

Value

A matrix

Numerical computations

The Information matrix (of the independent parameters) is computed using the Lystig and Hugues's algorithm. Then the covariance matrix is computed by inversion of this information matrix.

References

Lystig Theodore C. and Hugues James P. (2002) Exact Computation of the Observed Information Matrix for Hidden Markov Models, Journal of Computational and Graphical Statistics, Vol. 11, No 3, 678-689.

See Also

HMMFit

Examples

  data(n1d_3s)
  Res_n1d_3s<-HMMFit(obs_n1d_3s, nStates=3)
  covMat <- asymptoticCov(Res_n1d_3s, obs_n1d_3s)

RHmm documentation built on Nov. 30, 2023, 7:22 p.m.