M_step

This function performs the maximization step in the EAM
algorithm. More specifically, it maximizes the expected improvement.
ToDo: implement sample space contractions (see comment made in documentation
of <code>draw.sv.init</code>).

Several statistical methods for analyzing survival data under various forms of dependent
censoring are implemented in the package. In addition to accounting for dependent censoring, it
offers tools to adjust for unmeasured confounding factors. The implemented approaches allow
users to estimate the dependency between survival time and dependent censoring time, based
solely on observed survival data. For more details on the methods, refer to Deresa and Van
Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023)
<doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>,
Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Rutten et al. (2024+)
<doi:10.48550/arXiv.2403.11860> and Ding and Van Keilegom (2024).

Negera Wakgari Deresa

depCensoring

Statistical Methods for Survival Data with Dependent Censoring

Ilias Willems

Gilles Crommen

Jie Ding

Claudia Czado

Ingrid Van Keilegom

M_step function

<dl><dt>dir</dt>
<dd>Direction to search in. <code>dir = 1</code> corresponds to finding the
upper bound of the confidence interval. <code>dir = -1</code> corresponds to
finding the lower bound.</dd>
<dt>evaluations</dt>
<dd>Matrix containing each point that was already evaluated,
alongside the corresponding test statistic and critical value, as its rows.</dd>
<dt>theta.hash</dt>
<dd>Tentative best value of theta. Obtained from the E-step.</dd>
<dt>fit.krige</dt>
<dd>Kriging model obtained from the A-step.</dd>
<dt>test.fun</dt>
<dd>The test function to be inverted in order to obtain the
identified set.</dd>
<dt>c</dt>
<dd>Projection vector.</dd>
<dt>par.space</dt>
<dd>Bounds of the parameter space.</dd>
<dt>hyperparams</dt>
<dd>Parameters used in obtaining initial values
for the maximization algorithm. If <code>NULL</code>, default values are used.
Default is <code>hyperparams = NULL</code>.</dd>
<dt>verbose</dt>
<dd>Verbosity parameter.</dd></dl>

Arguments

M-step in the EAM algorithm described in KMS19. — M_step

<dl>

<dt>dir</dt>
<dd>Direction to search in. <code>dir = 1</code> corresponds to finding the
upper bound of the confidence interval. <code>dir = -1</code> corresponds to
finding the lower bound.</dd>


<dt>evaluations</dt>
<dd>Matrix containing each point that was already evaluated,
alongside the corresponding test statistic and critical value, as its rows.</dd>


<dt>theta.hash</dt>
<dd>Tentative best value of theta. Obtained from the E-step.</dd>


<dt>fit.krige</dt>
<dd>Kriging model obtained from the A-step.</dd>


<dt>test.fun</dt>
<dd>The test function to be inverted in order to obtain the
identified set.</dd>


<dt>c</dt>
<dd>Projection vector.</dd>


<dt>par.space</dt>
<dd>Bounds of the parameter space.</dd>


<dt>hyperparams</dt>
<dd>Parameters used in obtaining initial values
for the maximization algorithm. If <code>NULL</code>, default values are used.
Default is <code>hyperparams = NULL</code>.</dd>


<dt>verbose</dt>
<dd>Verbosity parameter.</dd>

</dl>

M_step: M-step in the EAM algorithm described in KMS19.

Description

Usage

Arguments