rmixar

Generates vectors of observations
from mixture of Gaussian linear (Markov-switching) autoregressive
model in a specified state and using the
parameters of a specified model

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

rmixar function

<dl><dt>j</dt>
<dd>a specified state</dd>
<dt>model</dt>
<dd>a <code>hhsmmspec</code> model</dd>
<dt>x</dt>
<dd>the previous x vector as the covariate of the autoregressive model</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

Random data generation from the mixture of Gaussian linear (Markov-switching) autoregressive models for hhsmm model — rmixar

<dl>

<dt>j</dt>
<dd>a specified state</dd>


<dt>model</dt>
<dd>a <code>hhsmmspec</code> model</dd>


<dt>x</dt>
<dd>the previous x vector as the covariate of the autoregressive model</dd>

</dl>

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

rmixar: Random data generation from the mixture of Gaussian linear (Markov-switching) autoregressive models for hhsmm model

Description

Usage

Value

Arguments

Author