which.peaks

Find local maxima and local minima of data sequence

Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Zhibing He

dSTEM

Multiple Testing of Local Extrema for Detection of Change Points

which.peaks function

<dl><dt>x</dt>
<dd>numerical vector contains local maxima (minima)</dd>
<dt>partial</dt>
<dd>logical value indicating if the two endpoints will be considered</dd>
<dt>decreasing</dt>
<dd>logical value indicating whether to find local minima</dd></dl>

Arguments

Find local maxima and local minima of data sequence — which.peaks

<dl>

<dt>x</dt>
<dd>numerical vector contains local maxima (minima)</dd>


<dt>partial</dt>
<dd>logical value indicating if the two endpoints will be considered</dd>


<dt>decreasing</dt>
<dd>logical value indicating whether to find local minima</dd>

</dl>

which.peaks: Find local maxima and local minima of data sequence

Description

Usage

Value

Arguments

Examples