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Power Computations for Intervention Analysis.

A I McLeod¹, E R Vingilis

¹Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario N6A 5B7, Canada, ( aimcleod@uwo.ca ).

Technometrics : a Journal of Statistics for the Physical, Chemical, and Engineering Sciences

|September 28, 2011

Summary

This summary is machine-generated.

This study introduces a power function to determine the probability of detecting meaningful changes in intervention analysis, especially when time series data is scarce. It provides methods for autoregressive integrated moving average (ARIMA) and fractional ARIMA models.

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Area of Science:

Time Series Analysis
Statistical Modeling
Intervention Analysis

Background:

Collecting time series data for intervention analysis can be costly and difficult.
Assessing the probability of detecting significant changes is crucial for study design.

Purpose of the Study:

To develop a methodology for computing the power function in intervention analysis.
To address challenges posed by expensive or limited time series data collection.

Main Methods:

Developed methodology for computing the power function for pulse, step, and ramp interventions.
Utilized autoregressive integrated moving average (ARIMA) and fractional ARIMA models for error structures.
Provided convenient formulas for special cases of the power function.

Main Results:

The power function can be computed for ARIMA and fractional ARIMA errors under various intervention types.
Formulas are simplified for specific, common scenarios.
Methodology is demonstrated with practical examples.

Conclusions:

The power function is a valuable tool for intervention analysis, particularly with limited data.
The developed methods enhance the ability to plan and interpret intervention studies.
Applications in traffic safety and environmental impact assessment are feasible.