IRLS_EM: Iterative Reweighted Least Squares for the EM algorithm

In regmhmm: 'regmhmm' Fits Hidden Markov Models with Regularization

Iterative Reweighted Least Squares for the EM algorithm

Description

Iterative Reweighted Least Squares algorithm for optimizing the parameters in the M-step of the EM algorithm.

Usage

IRLS_EM(X, gamma, Y, beta, family, eps_IRLS, max_N)

Arguments

X	A design matrix of size n x p.
gamma	A vector of size n specifying the posterior probability of the hidden states.
Y	A vector of observations of size n.
beta	A vector of size p + 1 specifying the GLM parameters.
family	The family of the response.
eps_IRLS	convergence tolerance in the iteratively reweighted least squares step.
max_N	the maximal number of IRLS iterations.

Value

A vector representing the estimates of beta.

Examples

## Not run: 
# Example usage of the function
IRLS_EM_one_step <- IRLS_EM_one(X,
                                gamma,
                                Y,
                                beta,
                                family)

## End(Not run)

regmhmm documentation built on May 29, 2024, 11:40 a.m.