coef.L0TFinvfix

coef.L0TFinvopt

Extract the coefficients of the estimated trends under the constraint of a given number of change points.

Trend filtering is a widely used nonparametric method for knot detection. This package provides an efficient solution for L0 trend filtering, avoiding the traditional methods of using Lagrange duality or Alternating Direction Method of Multipliers algorithms. It employ a splicing approach that minimizes L0-regularized sparse approximation by transforming the L0 trend filtering problem. The package excels in both efficiency and accuracy of trend estimation and changepoint detection in segmented functions. References: Wen et al. (2020) <doi:10.18637/jss.v094.i04>; Zhu et al. (2020)<doi:10.1073/pnas.2014241117>; Wen et al. (2023) <doi:10.1287/ijoc.2021.0313>.

Tianhao Wang

L0TFinv

A Splicing Approach to the Inverse Problem of L0 Trend Filtering

Canhong Wen

coef.L0TFinvfix function

<dl><dt>object</dt>
<dd>The output of L0TFinvfix or L0TFinvopt</dd>
<dt>k</dt>
<dd>The given number of change points</dd>
<dt>...</dt>
<dd>ignore</dd></dl>

Arguments

Extract estimated trends — coef.L0TFinvfix

<dl>

<dt>object</dt>
<dd>The output of L0TFinvfix or L0TFinvopt</dd>


<dt>k</dt>
<dd>The given number of change points</dd>


<dt>...</dt>
<dd>ignore</dd>

</dl>

coef.L0TFinvfix: Extract estimated trends

Description

Usage

Value

Arguments

See Also