detectAO {TSA} | R Documentation |
Additive Outlier Detection
Description
This function serves to detect whether there are any additive outliers
(AO). It implements the
test statistic proposed by Chang, Chen and Tiao (1988).
Usage
detectAO(object, alpha = 0.05, robust = TRUE)
Arguments
object |
a fitted ARIMA model |
alpha |
family significance level (5% is the default) Bonferroni rule is used to control the family error rate. |
robust |
if true, the noise standard deviation is estimated by mean absolute residuals times sqrt(pi/2). Otherwise, it is the estimated by sqrt(sigma2) from the arima fit. |
Value
A list containing the following components:
ind |
the time indices of potential AO |
lambda2 |
the corresponding test statistics |
Author(s)
Kung-Sik Chan
References
Chang, I.H., Tiao, G.C. and C. Chen (1988). Estimation of Time Series Parameters in the Presence of Outliers. Technometrics, 30, 193-204.
See Also
Examples
set.seed(12345)
y=arima.sim(model=list(ar=.8,ma=.5),n.start=158,n=100)
y[10]
y[10]=10
y=ts(y,freq=1,start=1)
plot(y,type='o')
acf(y)
pacf(y)
eacf(y)
m1=arima(y,order=c(1,0,0))
m1
detectAO(m1)
detectAO(m1, robust=FALSE)
detectIO(m1)
[Package TSA version 1.3.1 Index]