critvals

<p>This matrix contains critical values for the goodness of fit test
  for the last fitted value of a Repeated Median regression fit
  to a sample of size <code>n</code>. The critical values are based on the 0.95-quantiles
  of the distribution of a test statistic corresponding to the absolute value of
  the sum of a subset of residual signs. The critical value for a test based on the
  last <code>nI</code> out of <code>n</code> observations corresponds to <code>critvals[n,nI]</code>.</p>

datasets

Implementations for several robust procedures that allow for (online)
extraction of the signal of univariate or multivariate time series by
applying robust regression techniques to a moving time window are provided.
Included are univariate filtering procedures based on repeated-median
regression as well as hybrid and trimmed filters derived from it;
see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive
online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105>
and the slope comparing adaptive repeated median by Borowski and Fried (2013)
<doi:10.1007/s11222-013-9391-7> choose the width of the moving time
window adaptively. Multivariate versions are also provided; see
Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate
online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393>
for a multivariate slope comparing adaptive repeated median. Furthermore,
a repeated-median based filter with automatic outlier replacement and
shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.

Roland Fried

robfilter

Robust Time Series Filters

Karen Schettlinger

Matthias Borowski

Robin Nunkesser

Thorsten Bernholt

critvals function

<p>A (600x61)-matrix containing 30550 observations.</p>

Format

Critical Values for the RM Goodness of Fit Test — critvals

Critical Values for the RM Goodness of Fit Test

critvals: Critical Values for the RM Goodness of Fit Test

Description

Usage

Arguments

Format

References