Time series analysis is one of several branches of statistics whose practical importance has increased with the availability of powerful computing tools. Methodology originally developed for specialized applications, for example in business forecasting or geophysical signal processing, is now widely available in general statistical packages. These computing developments have helped to bring the subject closer to the mainstream of applied statistics. This book is an introductory account of time-series analysis, written from the perspective of an applied statistician with a particular interest in biological applications. Separate chapters cover exploratory methods, the theory of stationary random processes, spectral analysis, repeated measurements, ARIMA modelling, forecasting, and bivariate time-series analysis. Throughout, analyses of data-sets drawn from the biological and medical sciences are integrated with the methodological development. The book is unique in its emphasis on biological and medical applications of time-series analysis. Nevertheless, its methodological content is more widely applicable, and it should be useful to both students and practitioners of applied statistics, whatever their specialization. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

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Bibliographic Details

Title: Time Series: A Biostatistical Introduction (Oxford Statistical Science Series, No. 5)
Publisher: Oxford University Press, United Kingdom, Oxford
Publication Date: 1990
Language: English
ISBN 10: 0198522266
ISBN 13: 9780198522263
Binding: Paperback
Condition: Very Good
Weight: 485 grams

About this title

Synopsis

This book is an introductory account of time-series analysis, examined from the perspective of an applied statistician specializing in biological applications. It includes covers exploratory methods, including time-plots, smoothing, the correlogram and periodgram, as well as the theory of stationary random processes, spectral analysis and regression modelling, repeated measurements, ARIMA modelling, forecasting, and bivariate time series analysis. The book emphasizes biological and medical applications, but will be particularly useful to students and practitioners of applied statistics in all fields of application, as well as to biologists whose work involves the analysis of time-series data.

About the Author

Peter J. Diggle is at University of Lancaster.

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