Compute SNR of a certain change point location

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

snr function

<dl><dt>order</dt>
<dd>order of derivative of data</dd>
<dt>gamma</dt>
<dd>bandwidth of Gaussian kernel</dd>
<dt>is.jump</dt>
<dd>logical value indicating if the location to be calculated is a jump point</dd>
<dt>jump</dt>
<dd>jump height</dd>
<dt>diffb</dt>
<dd>difference of the slopes on left and right sides of the location</dd>
<dt>addb</dt>
<dd>sum of the slopes, only used when order is 1</dd></dl>

Arguments

Compute SNR of a certain change point location — snr

<dl>

<dt>order</dt>
<dd>order of derivative of data</dd>


<dt>gamma</dt>
<dd>bandwidth of Gaussian kernel</dd>


<dt>is.jump</dt>
<dd>logical value indicating if the location to be calculated is a jump point</dd>


<dt>jump</dt>
<dd>jump height</dd>


<dt>diffb</dt>
<dd>difference of the slopes on left and right sides of the location</dd>


<dt>addb</dt>
<dd>sum of the slopes, only used when order is 1</dd>

</dl>

snr: Compute SNR of a certain change point location

Description

Usage

Value

Arguments