arima.rob.object: Robust REGARIMA Model and Outliers Detection Objects

In robustarima: Robust ARIMA Modeling

Description

These are objects of class "arima.rob" which represent the robust fit of a regression model with ARIMA errors. It also contains information about the detected outliers.

Arguments

The following components must be included in a legitimate "arima.rob" object:

x	the model matrix.
y	the response variable.
model	a list with the following named components: "freq" which is the frequency of the original data, "sfreq" which is the seasonal frequency of the original data, "d" which is the number of regular differences, "sd" which is the number of seasonal differences, "ar" which is the estimated AR coefficients, "ma" which is the estimated MA coefficients, "sma" which is the seasonal MA coefficient if estimated.
regcoef	the estimates of regression coefficients.
regcoef.cov	the estimated covariance matrix of the regression coefficients.
innov	the estimated innovations.
innov.acf	a series whose autocorrelations or partial autocorrelations are the robust estimates of the innovation autocorrelations or partial autocorrelations.
regresid	the estimated regression residuals cleaned of additive outliers by the robust filter.
regresid.acf	a series whose autocorrelations or partial autocorrelations are the robust estimates of the autocorrelations or partial autocorrelations of the differenced regression residuals.
sigma.innov	a robust estimate of the innovation scale.
sigma.regresid	an estimate of the scale of the differenced regression residuals.
sigma.first	the first estimate of the innovation scale based only on the scale of the differenced model and the ARMA parameters.
tuning.c	the bandwidth of the robust filter.
y.robust	the response series cleaned of outliers by the robust filter.
y.cleaned	the response series cleaned of additive outliers and level shifts after the outliers detection procedure.
predict.error	the fitted and predicted regression errors.
predict.scales	the standard deviations of the fitted and predicted regression errors.
n.predict	the number of predicted observations, which is equal to the n.predict argument passed to the arima.rob function that produced the "arima.rob" object.
tauef	the inverse of the estimated efficiency factor of the tau-estimate with respect to the LS-estimate.
inf	information about the outcome of the last optimization procedure: inf=1 indicates that the procedure converged, and inf=0 that the procedure did not converge.
innov.outlier	logical flag, the same as the innov.outlier argument passed to the arima.rob function that produced the "arima.rob" object.
outliers	an object of class "outliers", which contains all the detected outliers (and level shifts).
outliers.iter	optionally a list of objects of class "outliers", if the iter argument passed to the arima.rob function that produced the "arima.rob" object is non-zero.
n0	the number of missing innovations at the beginning.
call	an image of the call that produced the object, but with the arguments all named and with the actual formula included as the formula argument.
assign	the same as the assign component of an "lm" object.
contrasts	the same as the contrasts component of an "lm" object.
terms	the same as the terms component of an "lm" object.
rank	the same as the rank component of an "lm" object.

Generation

This class of objects is returned from the arima.rob function.

Methods

coef, formula, outliers, predict, print, summary.

See Also

arima.rob, outliers, outliers.object.

robustarima documentation built on May 1, 2021, 1:06 a.m.