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Power Computations for Intervention Analysis

A. Ian McLeod
Organization: University of Western Ontario
Department: Department of Statistical and Actuarial Sciences
URL: http://www.stats.uwo.ca/faculty/aim/
E.R. Vingilis
Journal / Anthology

Year: 2005
Volume: 47
Page range: 174-180

In many intervention analysis applications time series data may be expensive or otherwise diffcult to collect. In this case the power function is helpful since it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming that an underlying ARIMA or fractional ARIMA model is known or can be estimated from the pre-intervention time series, the methodology for computing the required power function is developed for pulse, step and ramp interventions with ARIMA and fractional ARIMA errors. Convenient formulae for computing the power function for important special cases are given. Illustrative applications in tra▒c safety and environmental impact assessment are discussed.

*Mathematics > Probability and Statistics

Statistical power, Intervention Analysis, Autocorrelation and lack of statistical independence, ARIMA time series models, Environmental impact assessment, Forecast, and actuality signi»cance test, Long-memory time series, Sample size, Two-sample problem.


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