miss_mixmvnorm_mstep

The M step function of the EM algorithm for the mixture
of multivariate normals as the emission distribution with
missing values using the observation matrix and the estimated
weight vectors

Develops algorithms for fitting, prediction, simulation
and initialization of the following models
(1)- hidden hybrid Markov/semi-Markov model,
introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>,
(2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015
<doi:10.1111/biom.12282>),
(3)- regime switching regression model
(Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive
hidden hybrid Markov/semi-Markov model,
(4)- spline-based nonparametric
estimation of additive state-switching models
(Langrock et al., 2018 <doi:10.1111/stan.12133>)
(5)- robust emission model proposed by
Qin et al, 2024 <doi:10.1007/s10479-024-05989-4>
(6)- several emission distributions, including mixture of multivariate normal
(which can also handle missing data using EM algorithm) and multi-nomial emission
(for modeling polymer or DNA sequences)
(7)- tools for prediction of future state sequence, computing the score of a new sequence,
splitting the samples and sequences to train and test sets, computing the information measures of
the models, computing the residual useful lifetime (reliability) and many other useful tools ...
(read for more description: Amini et al., 2022 <doi:10.1007/s00180-022-01248-x> and its
arxiv version: <doi:10.48550/arXiv.2109.12489>).

Morteza Amini

hhsmm

Hidden Hybrid Markov/Semi-Markov Model Fitting

Afarin Bayat

Reza Salehian

miss_mixmvnorm_mstep function

<dl><dt>x</dt>
<dd>the observation matrix which can contain missing values (NA or NaN)</dd>
<dt>wt1</dt>
<dd>the state probabilities matrix (number of observations
times number of states)</dd>
<dt>wt2</dt>
<dd>the mixture components probabilities list (of length
nstate) of matrices (number of observations times number of
mixture components)</dd>
<dt>par</dt>
<dd>the parameters of the model in the previous step of
the EM algorithm. For initialization of the model when the data
is initially imputed, <code>par</code> can be NULL</dd></dl>

Arguments

Morteza Amini, <a href="/link/morteza.amini%40ut.ac.ir?package=hhsmm&version=0.4.2" data-mini-rdoc="hhsmm::morteza.amini@ut.ac.ir">morteza.amini@ut.ac.ir</a>

Author

the M step function of the EM algorithm — miss_mixmvnorm_mstep

<dl>

<dt>x</dt>
<dd>the observation matrix which can contain missing values (NA or NaN)</dd>


<dt>wt1</dt>
<dd>the state probabilities matrix (number of observations
times number of states)</dd>


<dt>wt2</dt>
<dd>the mixture components probabilities list (of length
nstate) of matrices (number of observations times number of
mixture components)</dd>


<dt>par</dt>
<dd>the parameters of the model in the previous step of
the EM algorithm. For initialization of the model when the data
is initially imputed, <code>par</code> can be NULL</dd>

</dl>

Morteza Amini, <a href='mailto:morteza.amini@ut.ac.ir'>morteza.amini@ut.ac.ir</a>

miss_mixmvnorm_mstep: the M step function of the EM algorithm

Description

Usage

Value

Arguments

Author

Examples