est.pair

Identify pairwise local maxima and local minima of the second-order derivative

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

est.pair function

<dl><dt>vall</dt>
<dd>vector of locations of significant local minima</dd>
<dt>peak</dt>
<dd>vector of locations of significant local maxima</dd>
<dt>gamma</dt>
<dd>bandwidth of Gaussian kernel smoothing function</dd></dl>

Arguments

Identify pairwise local maxima and local minima of the second-order derivative — est.pair

<dl>

<dt>vall</dt>
<dd>vector of locations of significant local minima</dd>


<dt>peak</dt>
<dd>vector of locations of significant local maxima</dd>


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

</dl>

est.pair: Identify pairwise local maxima and local minima of the second-order derivative

Description

Usage

Value

Arguments