extremal.index.sliding.blocks

a numeric value giving the length of blocks. Default is <code>b = round(sqrt(n))</code>.

Estimation of an extremal index using the sliding blocks estimator suggested in (Northrop, 2015).
The function is called by <code><a rd-options="" href="/link/KRDetect.outliers.EV?package=envoutliers&version=1.1.0" data-mini-rdoc="envoutliers::KRDetect.outliers.EV">KRDetect.outliers.EV</a></code> and is not intended for use by regular users of the package.

Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).

Martina Campulova

envoutliers

Methods for Identification of Outliers in Environmental Data

Roman Campula

extremal.index.sliding.blocks function

Estimation of an extremal index using the sliding blocks estimator suggested in (Northrop, 2015).
The function is called by <code><a rd-options='' href='KRDetect.outliers.EV'>KRDetect.outliers.EV</a></code> and is not intended for use by regular users of the package.

extremal.index.sliding.blocks: Extremal index estimation (Northrop, 2015) - Only intended for developer use

Description

Usage

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References