A Sensitivity Study of Intervention Analysis for the Identification of an Environmental Event
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Robert Wharton, Strategy and Statistics Department, Fordham University, New York, USA.
This paper presents a sensitivity analysis for the application of intervention analysis applied to environmental time series data to determine the probability of identifying a significant environmental event for various sample sizes. These determinations will be carried out using simulations involving 10,000 replications generated using the “R” programming Language.
Intervention Analysis, Transfer Function, Simulation
[1]
Box, G. E. P. and Tiao, G. C., (1965) "A change in Level of a Non-Stationary Time Series", Biometrika, 52, pp 181-192.
[2]
Box, G. E. P. and Tiao, G. C., (1975) “Intervention Analysis with Applications to Economic and Environmental Problems”, Journal of the American Statistical Association, 70, pp 70–79.
[3]
Abraham, A. (1987) "Applications of Intervention Analysis to a Road Fatality Series in Ontario", Journal of Forecasting, 6, pp 211-219.
[4]
Vandaele, W. (1983) Applied Time Series and Box-Jenkins Models, Academic Press, Orlando, FL.
[5]
Glass, G. V. (1972) "Estimating the E⁄ects of Intervention into a NonStationary Time Series", American Education Research Journal, 9, no. 3, pp 463-477.
[6]
Ansley, C. F. (1979) "An Algorithm for the Exact Likelihood of a Mixed Autoregressive Moving Average Process", Biometrika", 66, pp 59-65.
[7]
Karner, O. (2002) “On Nonstationary and Antipersistency in Global Temperature Series”, Journal of Geophysical Research. 107, D20, pp 399-410.
[8]
Reinsel, G. C. (2002), “Trend Analysis of Upper Stratospheric Umkehr Ozone Data for Evidence of Turnaround”. Geophysical Research Letters, 29, pp 174-195.
[9]
Bell R, Glinianaia SV, Waal ZVD, et al (2016) “Evaluation of a complex healthcare intervention to increase smoking cessation in pregnant women: interrupted time series analysis with economic evaluation”, Tobacco Control Published Online First: 15 February 2017. doi: 10.1136/tobaccocontrol-2016-053476.
[10]
Hipel, K. W., McLeod, A. I. (1994) Time Series Modeling of Water Resources and Environmental Systems, Amsterdam: Elsevier.
[11]
Salam, A. W., Rado, Y., Suhartono, M. Moh, D. H. G. (2017) “Multi-Input Intervention Analysis for evaluating the Domestic Airline Passengers in an International Airport”, Science Journal of Applied Mathematics and Statistics, 5, pp 110-126.
[12]
Matarise F. (2014) Intervention Analysis in Time Series. In: Lovric M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg.
[13]
Garziano, G. (2017) “ARIMA Models and Intervention Analysis”. Data Science+, pp 87-95.
[14]
Cowperwait, P, Metcalfe, A. (2009) Introduction to Time Series with R,, New York, NY.
[15]
Rossiter, D. G. (2016) Tutorial: Time Series Analysis in R, ORCID: 0000-0003-4558-1286.
[16]
Derryberry, D. (2014) Basic Data Analysis for Time Series with R, John Wiley, New York, New York.
[17]
McLeod, A. I., Vingilis, E. R. (2005) “Power Computations for Intervention Analysis”, Technometrics, vol. 42, pp 174-181.
[18]
Lenth, R. V. (2001 “Some Practical Guidelines for Effective Sample Size Determination”, The American Statistician, 55, pp 187-193.
