cv.tuning.selection

a function to apply cross-validation to select tuning parameter by minimizing SSE

internal

Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) <doi:10.1080/01621459.2020.1770097> and Bai, Safikhani and Michailidis (2020) <doi:10.1109/TSP.2020.2993145>.

Yue Bai

VARDetect

Multiple Change Point Detection in Structural VAR Models

Peiliang Bai

Abolfazl Safikhani

George Michailidis

cv.tuning.selection function

<dl><dt>data</dt>
<dd>a n by p dataset matrix</dd>
<dt>lambda.seq</dt>
<dd>a numeric vector, indicates the sequence of tuning parameters of sparse components</dd>
<dt>mu.seq</dt>
<dd>a numeric vector, the sequence of tuning parameters of low rank components</dd>
<dt>alpha_L</dt>
<dd>a positive numeric value, indicating the constraint space of low rank components</dd>
<dt>nfold</dt>
<dd>a positive integer, the number of folds for cv</dd></dl>

Arguments

a function to apply cross-validation to select tuning parameter by minimizing SSE — cv.tuning.selection

<dl>

<dt>data</dt>
<dd>a n by p dataset matrix</dd>


<dt>lambda.seq</dt>
<dd>a numeric vector, indicates the sequence of tuning parameters of sparse components</dd>


<dt>mu.seq</dt>
<dd>a numeric vector, the sequence of tuning parameters of low rank components</dd>


<dt>alpha_L</dt>
<dd>a positive numeric value, indicating the constraint space of low rank components</dd>


<dt>nfold</dt>
<dd>a positive integer, the number of folds for cv</dd>

</dl>

cv.tuning.selection: a function to apply cross-validation to select tuning parameter by minimizing SSE

Description

Usage

Value

Arguments