Accessibility navigation


Modelling seasonally varying data: A case study for sudden infant death syndrome (SIDS)

Mooney, J. A., Jolliffe, I. T. and Helms, P. J. (2006) Modelling seasonally varying data: A case study for sudden infant death syndrome (SIDS). Journal of Applied Statistics, 33 (5). pp. 535-547. ISSN 0266-4763

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Abstract/Summary

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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:5314
Uncontrolled Keywords:cardioid distribution circular data cosinor analysis regression seasonality SIDS von Mises distribution

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation