bivph_laplace

Bivariate phase-type joint Laplace

Tools for phase-type distributions including the following variants:
continuous, discrete, multivariate, in-homogeneous, right-censored, and regression.
Methods for functional evaluation, simulation and estimation using the
expectation-maximization (EM) algorithm are provided for all models.
The methods of this package are based on the following references.
Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm,
Olsson, M. (1996). Estimation of phase-type distributions from censored data,
Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>,
Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>,
Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>,
Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>,
Bladt, M. (2022) <doi:10.1017/asb.2021.40>,
Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>,
Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>,
Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Martin Bladt

matrixdist

Statistics for Matrix Distributions

Jorge Yslas

Alaric Müller

bivph_laplace function

<dl><dt>r</dt>
<dd>Matrix of values.</dd>
<dt>alpha</dt>
<dd>Vector of initial probabilities.</dd>
<dt>S11</dt>
<dd>Sub-intensity matrix.</dd>
<dt>S12</dt>
<dd>Matrix.</dd>
<dt>S22</dt>
<dd>Sub-intensity matrix.</dd></dl>

Arguments

Bivariate phase-type joint Laplace — bivph_laplace

<dl>

<dt>r</dt>
<dd>Matrix of values.</dd>


<dt>alpha</dt>
<dd>Vector of initial probabilities.</dd>


<dt>S11</dt>
<dd>Sub-intensity matrix.</dd>


<dt>S12</dt>
<dd>Matrix.</dd>


<dt>S22</dt>
<dd>Sub-intensity matrix.</dd>

</dl>

bivph_laplace: Bivariate phase-type joint Laplace

Description

Usage

Value

Arguments