Modelling seasonally varying data

a case study for sudden infant death syndrome (SIDS)

J A Mooney, I T Jolliffe, Peter Joseph Benedict Helms

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)535-547
Number of pages13
JournalJournal of Applied Statistics
Volume33
Issue number5
Early online date16 Aug 2006
DOIs
Publication statusPublished - 2006

Keywords

  • cardioid distribution
  • circular data
  • cosinor analysis
  • regression
  • seasonality
  • SIDS
  • von Mises distribution

Cite this

Modelling seasonally varying data : a case study for sudden infant death syndrome (SIDS). / Mooney, J A ; Jolliffe, I T ; Helms, Peter Joseph Benedict.

In: Journal of Applied Statistics, Vol. 33, No. 5, 2006, p. 535-547.

Research output: Contribution to journalArticle

Mooney, J A ; Jolliffe, I T ; Helms, Peter Joseph Benedict. / Modelling seasonally varying data : a case study for sudden infant death syndrome (SIDS). In: Journal of Applied Statistics. 2006 ; Vol. 33, No. 5. pp. 535-547.
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