Description Usage Arguments Value Warning References See Also Examples
Returns an object of class "arima.rob"
that represents a robust fit of a linear
regression model with ARIMA errors using a filtered tau-estimate. The error
model may have seasonal differences and one seasonal moving average
parameter. It also returns the detected outliers and level shifts.
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formula |
a |
data |
a data frame or a |
contrasts |
the same as the |
start |
a character string which can be passed to |
end |
a character string which can be passed to |
p |
the autoregressive order of the errors model.
The default is |
q |
the moving average order of the errors model.
The default is |
d |
the number of regular differences in the ARIMA model.
It must be |
sd |
the number of seasonal differences. It must be |
freq |
the frequency of |
sfreq |
the seasonality frequency of |
sma |
logical flag: if |
auto.ar |
logical flag: If |
max.p |
the maximum order of the autoregressive stationary model that approximates
the ARMA stationary model. If |
n.predict |
the maximum number of future periods for which we wish to compute the
predictions. The default is |
tol |
the tolerance for convergence. |
max.fcal |
the maximum number of function evaluations. |
iter |
a logical flag or the number of iterations to
execute |
innov.outlier |
logical flag: if |
critv |
the critical value for detecting outliers.
If |
... |
extra arguments passed to or from other methods. |
an object of class "arima.rob"
representing the fit and the outliers detected.
See arima.rob.object
for details of
the components of the object.
When either d
or
sd
is greater than zero, the interpretation
of the intercept in the formula
is
different from its usual interpretation:
it represents the coefficient of the lowest order power of the time trend
which can be identified. For example, if
d=2
and
sd=0
, the
intercept represents the coefficient of the
term t^2
.
Bianco, A., Garcia Ben, M., Martinez, E., and Yohai, V. (1996). Robust procedures for regression models with ARIMA errors. COMPSTAT 96, Proceedings in Computational Statistics. Ed. Albert Prat, pages. 27-38. Physica-Verlag, Heidelberg.
Bianco, A., Garcia Ben, M., Martinez, E., and Yohai, V. (1997). Outlier detection in regression models with ARIMA errors using robust estimates. mimeo.
Chang, I., Tiao, G. C., and Chen, C. (1988). Estimation of time series parameters in the presence of outliers. Technometrics, 30:193-204.
Martin, R. D., Samarov, A., and Vandaele, W. (1983). Robust methods for ARIMA models. in Applied Time Series Analysis of Economic Data, E. Zellner, ed.
Yohai, V. Y., and Zamar, R. H. (1988). High breakdown-point estimates of regression by means of the minimization of an efficient scale. Journal of the American Statistical Association, 83:406-413.
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