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A platform for research: civil engineering, architecture and urbanism
Estimating trend from seasonal data: is daily, monthly or annual data best?
We consider a model for time series whose mean has a component that is linear in time with slope b plus seasonal sinusoidal components. Suppose we have N years of observations and J observations per year. We answer the question: what is the loss in efficiency of estimating b if the J observations are grouped into weekly means, monthly means, annual means, and so on? We give answers for the cases of a single sinusoidal component and q sinusoidal components. We derive tests of hypotheses, confidence intervals, and power functions for the slope. Finally, an application of the results to daily minimum and daily maximum temperature data from Auckland is presented. Copyright © 2015 John Wiley & Sons, Ltd.
Estimating trend from seasonal data: is daily, monthly or annual data best?
We consider a model for time series whose mean has a component that is linear in time with slope b plus seasonal sinusoidal components. Suppose we have N years of observations and J observations per year. We answer the question: what is the loss in efficiency of estimating b if the J observations are grouped into weekly means, monthly means, annual means, and so on? We give answers for the cases of a single sinusoidal component and q sinusoidal components. We derive tests of hypotheses, confidence intervals, and power functions for the slope. Finally, an application of the results to daily minimum and daily maximum temperature data from Auckland is presented. Copyright © 2015 John Wiley & Sons, Ltd.
Estimating trend from seasonal data: is daily, monthly or annual data best?
Withers, Christopher S. (author) / Nadarajah, Saralees (author)
Environmetrics ; 26 ; 488-501
2015-11-01
14 pages
Article (Journal)
Electronic Resource
English
power , efficiency , trend
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