Simple Robust Tests for Autocorrelated Errors in Time Series Design Intervention Models |
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Authors: | Oluwagbohunmi A Awosoga Joseph W Mckean Bradley E Huitema |
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Institution: | 1. Addictions Counselling, University of Lethbridge, Lethbridge, Alberta, Canada;2. Department of Statistics, Western Michigan University, Kalamazoo, Michigan, USA;3. Department of Psychology, Western Michigan University, Kalamazoo, Michigan, USA |
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Abstract: | A simple, robust test for the autocorrelation parameter in an intervention time-series model (AB design) is proposed. It is analogous to the traditional tests and can easily be computed by using the freeware R. In the same way as traditional tests of autocorrelation are based on least squares (LS) fits of a linear model, our robust test is based on the highly efficient Wilcoxon fit of the linear model. We present the results of a Monte Carlo study which show that our robust test inherits the good efficiency properties of this Wilcoxon fit. Its empirical power is only slightly less than the empirical power of the least squares test over situations with normally distributed errors while it exhibited much more power over situations with error distributions having tails heavier than those of a normal distribution. It also showed robustness of validity over all null situations simulated. We also present the results of the application of our test to a real data set which illustrates the robustness of our test. |
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Keywords: | AB-Design Contaminated normal distribution Level change Monte Carlo study Sensitivity study Slope change Wilcoxon fit Mathematics Subject Classification |
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