TSA (version 1.3)

detectAO: Additive Outlier Detection

Description

This function serves to detect whether there are any additive outliers (AO). It implements the test statistic \(lambda_{2,t}\) 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

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

detectIO

Examples

# NOT RUN {
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)
# }