MRC.asymptotic.dyadic

Simulated values of the MRC statistic with <code>penalty="sqrt"</code> based on dyadic interval lengths computed from Gaussian white noise sequences of ("almost infinite") length 100,000.
Deprecation warning: This data set is needed for <code>smuceR</code> and may be removed when this function will be removed.

datasets

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

Pein Florian

stepR

Multiscale Change-Point Inference

Thomas Hotz

Hannes Sieling

Timo Aspelmeier

MRC.asymptotic.dyadic function

A <code><a href="/link/numeric?package=stepR&version=2.1-10" data-mini-rdoc="stepR::numeric">numeric</a></code> vector containing 10,000 sorted values.

Format

"Asymptotic" values of the MRC statistic (dyadic intervals) — MRC.asymptotic.dyadic

A <code><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></code> vector containing 10,000 sorted values.

MRC.asymptotic.dyadic: "Asymptotic" values of the MRC statistic (dyadic intervals)

Description

Usage

Arguments

Format

Examples