Special Issue of the Journal of Time Series Analysis: Time Series Analysis in the Biological Sciences

Features

  • Date: 30 Aug 2012

The types of problems encountered in analyzing time series or spatial processes (or both) from the biological sciences are about as broad as the field itself. This includes applications in molecular biology, environmental biology, epidemiology, neurology and bioinformatics, marine biology, oceanography, biotechnology, physiology, botany, ecology, medicine, and evolution. Many of the problems involve departures from linearity, normality, and stationarity or homogeneity, and may involve missing data, irregular sampling, or multiple series collected at different time scales. Moreover, many current biological time series are collected under designed experiments and thus require modelling the between-subject and between-trial variations.

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The collection of 12 invited articles included in issue 33:5 of the Journal of Time Series Analysis guest edited by David S. Stoffer (University of Pittsburgh) and Hernando Ombao (University of California, Irvine) are meant to demonstrate the variety of problems and approaches that are taken to solve complex problems in the biological sciences. The techniques run the gamut of time series techniques, and include analyses in the time, spatial, and frequency domains. The hope is that we may motivate more experts in time series and spatial analysis to consider working on problems in the biological sciences. As will be seen from the collection, the problems are many and are rich. In fact, comprehensive solutions to these problems may require statistical techniques from a variety of areas including functional data analysis, mixed effects models, high dimensional data analysis, statistical learning and computing.

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