Sum and Sup Scaling Outlier Tests

Computes the Sum and Supremum Scaling Tests for the overall presence of outliers based on Jiao and Pretis (2019).

Usage

outlierscaletest(x, nsim = 10000)

Arguments

x: list, output of the isatloop function
nsim: integer, number of replications to simulate critical values for the Sup test

Details

The function takes the output of the isatloop function and computes the Scaling Sum and Supremum Tests for the presence of outliers from Jiao and Pretis (2019). The test compares the expected and observed proportion of outliers over the range of different significance levels of selection specified in isatloop. The Sum test compares the sum of deviations against the standard normal distribution, the Sup test compares the supremum of deviations against critical values simulated with nsim replications. The null hypothesis is that the observed proportion of outliers scales with the proportion of outliers under the null of no outliers.

Value

Returns a list of two htest objects. The first providing the results of the Sum test on the sum of the deviation of outliers against a standard normal distribution. The second providing the results on the supremum of the deviation of outliers against simulated critical values.

References

Jiao, X. & Pretis, F. (2019). Testing the Presence of Outliers in Regression Models. Discussion Paper.

Pretis, F., Reade, J., & Sucarrat, G. (2018). Automated General-to-Specific (GETS) regression modeling and indicator saturation methods for the detection of outliers and structural breaks. Journal of Statistical Software, 86(3).

Author

Xiyu Jiao, & Felix Pretis, http://www.felixpretis.org/

Examples

  ###Repeated isat models using the Nile dataset
  ### where p-values are chosen such that the expected number of outliers under the null
  ### corresponds to 1, 2, ..., 20. Then computing the Outlier Scaling Tests:
  
  #nile <- as.zoo(Nile)
  #isat.nile.loop <- isatloop(y=nile)
  #outlierscaletest(isat.nile.loop)