Oxford, [Oxfordshire] : Clarendon Press ; New York : Oxford University Press, 1990.
xi, 257 p. : ill. ; 24 cm.
Includes bibliographical references and index.
Introduction-- 1. Simple descriptive methods of analysis-- 2. Theory of stationery processes-- 3. Spectral analysis-- 4. Repeated measurements-- 5. Fitting autoregressive moving average processes to data-- 6. Forecasting-- 7. Elements of bivariate time-series analysis-- References-- Appendix A, B & C.
(source: Nielsen Book Data)
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. (source: Nielsen Book Data)