 Modelling seasonally varying data: A case study for Sudden Infant Death Syndrome (SIDS)Jennifer Mooney, Ian Jolliffe and Peter Helms Journal of Applied Statistics, 2006, vol. 33, issue 5, 535-547 Abstract: Many time series are measured monthly, either as averages or totals, and such data often exhibit seasonal variability - the values of the series are consistently larger for some months of the year than for others. A typical series of this type is the number of deaths each month attributed to SIDS (Sudden Infant Death Syndrome). Seasonality can be modelled in a number of ways. This paper describes and discusses various methods for modelling seasonality in SIDS data, though much of the discussion is relevant to other seasonally varying data. There are two main approaches, either fitting a circular probability distribution to the data, or using regression-based techniques to model the mean seasonal behaviour. Both are discussed in this paper. Keywords: Cardioid distribution; circular data; cosinor analysis; regression; seasonality; SIDS; von Mises distribution (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:33:y:2006:i:5:p:535-547 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Applied Statistics is currently edited by Robert Aykroyd More articles in Journal of Applied Statistics from Taylor & Francis Journals |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.