Time series : a biostatistical introduction
 Responsibility
 Peter J. Diggle.
 Language
 English.
 Imprint
 Oxford, [Oxfordshire] : Clarendon Press ; New York : Oxford University Press, 1990.
 Physical description
 xi, 257 p. : ill. ; 24 cm.
 Series
 Oxford statistical science series 5
Access
Available online
Marine Biology Library (Miller)
Stacks
Call number  Status 

QA280 .D54 1990  Unknown 
Math & Statistics Library
Stacks
Call number  Status 

QA280 .D54 1990  Unknown 
More options
Creators/Contributors
 Author/Creator
 Diggle, Peter.
Contents/Summary
 Bibliography
 Includes bibliographical references and index.
 Contents

 Part 1 Introduction: definitions and notation objectives of timeseries analysis more notation trend, serial dependence and stationarity duality between trend and serial dependence software. Part 2 Simple descriptive methods of analysis: timeplots smoothing differencing the autocovariance and autocorrelation functions estimating the autocorrelation function impact of trendremoval on autocorrelation structure the periodogram the connection between the correlogram and the periodogram. Part 3 Theory of stationary processes: notation and definitions the spectrum of a stationary random process linear filters the autoregressive moving average process sampling and accumulation of stationary random functions implications of autocorrelation for elementary statistical methods. Part 4 Spectral analysis: the periodogram revisited periodogrambased tests of white noise the fast Fournier transform periodogram averages other smooth estimates of the spectrum adjusting spectral estimates for the effects of filtering combining and comparing spectral estimates fitting parametric models strengths and weaknesses of spectral analysis. Part 5 Repeated measurements: repeated measurements as multivariate data incorporating timeseries structure formulating the model  timeplots and the variogram fitting the model  analysis of data on protein content of milk samples. Part 6 Fitting autoregressive moving average processes to data: ARIMA processes as models for nonstationary timeseries identification estimation diagnostic checking casestudies. Part 7 Forecasting: preamble forecasting by extrapolation of polynomial trends exponential smoothing the BoxJenkins approach to forecasting. Part 8 Elements of bivariate timeseries analysis: the crosscovariance and crosscorrelation functions estimating the crosscorrelation function the spectrum of a bivariate process estimating the crossspectrum.
 (source: Nielsen Book Data)9780198522065 20160527
 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 timeseries analysis References Appendix A, B & C.
 (source: Nielsen Book Data)9780198522263 20160528
 Publisher's Summary
 Timeseries 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, written from the perspective of an applied statistician interested in biological applications and throughout analyses of datasets drawn from the biological and medical sciences are integrated with the methodological development.
(source: Nielsen Book Data)9780198522065 20160527  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 timeseries 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 timeseries analysis. Throughout, analyses of datasets 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 timeseries 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)9780198522263 20160528
Subjects
Bibliographic information
 Publication date
 1990
 ISBN
 0198522061 : $52.50
 0198522266 (pbk.) : $22.50
 9780198522065
 9780198522263 (pbk.)