modifChol

Computes the revised modified Cholesky factorization described in Schnabel and Eskow (1999).

Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <DOI:10.48550/arXiv.1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.

Sheila Goerz

robcp

Robust Change-Point Tests

Alexander Duerre

Roland Fried

modifChol function

<dl> <dt>x</dt>
<dd>a symmetric matrix.</dd> <dt>tau</dt>
<dd>(machine epsilon)^(1/3).</dd> <dt>tau_bar</dt>
<dd>(machine epsilon^(2/3)).</dd> <dt>mu</dt>
<dd>numeric, \(0 &lt; \mu \le 1\).</dd></dl>

Arguments

Author

Revised Modified Cholesky Factorization — modifChol

<dl>

 <dt>x</dt>
<dd>a symmetric matrix.</dd>

 <dt>tau</dt>
<dd>(machine epsilon)^(1/3).</dd>

 <dt>tau_bar</dt>
<dd>(machine epsilon^(2/3)).</dd>

 <dt>mu</dt>
<dd>numeric, \(0 &lt; \mu \le 1\).</dd>

</dl>

modifChol: Revised Modified Cholesky Factorization

Description

Usage

Value

Arguments

Author

Details

References

Examples