online.univar

Perform online change point detection with controlled false alarm rate or average run length.

Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.

Haotian Xu

changepoints

A Collection of Change-Point Detection Methods

Oscar Padilla

Daren Wang

Mengchu Li

Qin Wen

online.univar function

<dl><dt>y_vec</dt>
<dd>A <code>numeric</code> vector of observations.</dd>
<dt>b_vec</dt>
<dd>A <code>numeric</code> vector of thresholds b_t with t &gt;= 2.</dd>
<dt>train_vec</dt>
<dd>A <code>numeric</code> vector of training data from a pre-change distribution (no change point), which is only needed to when b_vec is NULL in order to calibrate b_t.</dd>
<dt>alpha</dt>
<dd>A <code>numeric</code> scalar of desired false alarm rate.</dd>
<dt>gamma</dt>
<dd>An <code>integer</code> scalar of desired average run length.</dd>
<dt>permu_num</dt>
<dd>An <code>integer</code> scalar of number of random permutation for calibration.</dd></dl>

Arguments

Author

Online change point detection with controlled false alarm rate or average run length. — online.univar

<dl>

<dt>y_vec</dt>
<dd>A <code>numeric</code> vector of observations.</dd>


<dt>b_vec</dt>
<dd>A <code>numeric</code> vector of thresholds b_t with t &gt;= 2.</dd>


<dt>train_vec</dt>
<dd>A <code>numeric</code> vector of training data from a pre-change distribution (no change point), which is only needed to when b_vec is NULL in order to calibrate b_t.</dd>


<dt>alpha</dt>
<dd>A <code>numeric</code> scalar of desired false alarm rate.</dd>


<dt>gamma</dt>
<dd>An <code>integer</code> scalar of desired average run length.</dd>


<dt>permu_num</dt>
<dd>An <code>integer</code> scalar of number of random permutation for calibration.</dd>

</dl>

online.univar: Online change point detection with controlled false alarm rate or average run length.

Description

Usage

Value

Arguments

Author

References

Examples