Time Series: A Biostatistical Introduction

Paperback | April 30, 1999

byPeter J. Diggle

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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 nowwidely 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, repeatedmeasurements, 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.

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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 nowwidely available in general statistical pack...

Peter J. Diggle is at University of Lancaster.

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Format:PaperbackDimensions:268 pages, 9.21 × 6.14 × 0.59 inPublished:April 30, 1999Publisher:Oxford University Press

The following ISBNs are associated with this title:

ISBN - 10:0198522266

ISBN - 13:9780198522263

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Table of Contents

Introduction1. Simple descriptive methods of analysis2. Theory of stationery processes3. Spectral analysis4. Repeated measurements5. Fitting autoregressive moving average processes to data6. Forecasting7. Elements of bivariate time-series analysisReferencesAppendix A, B and C

Editorial Reviews

'The particular appeal of the book to readers of this journal will be the way in which real biological data sets are used to illuminate the theory.'Biometrics, December 1993