Time series : a biostatistical introduction

Author(s)

• Diggle, Peter

Bibliographic Information

Time series : a biostatistical introduction

Peter J. Diggle

（Oxford statistical science series, 5）（Oxford science publications）

Clarendon Press, 1990

• : hard
• : pbk

Note

Includes bibliographical references and indexes

Description and Table of Contents

Volume

: hard ISBN 9780198522065

Description

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, written from the perspective of an applied statistician interested in biological applications and throughout analyses of data-sets drawn from the biological and medical sciences are integrated with the methodological development.

Table of Contents

• Part 1 Introduction: definitions and notation
• objectives of time-series analysis
• more notation
• trend, serial dependence and stationarity
• duality between trend and serial dependence
• software. Part 2 Simple descriptive methods of analysis: time-plots
• smoothing
• differencing
• the autocovariance and autocorrelation functions
• estimating the autocorrelation function
• impact of trend-removal 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
• periodogram-based 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 time-series structure
• formulating the model - time-plots 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 non-stationary time-series
• identification
• estimation
• diagnostic checking
• case-studies. Part 7 Forecasting: preamble
• forecasting by extrapolation of polynomial trends
• exponential smoothing
• the Box-Jenkins approach to forecasting. Part 8 Elements of bivariate time-series analysis: the cross-covariance and cross-correlation functions
• estimating the cross-correlation function
• the spectrum of a bivariate process
• estimating the cross-spectrum.

Volume

: pbk ISBN 9780198522263

Description

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.

Table of Contents

• 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

by "Nielsen BookData"

Details

• NCID: BA10037576
• ISBN:
  • 0198522061
  • 0198522266
• LCCN: 89034018
• Country Code: uk
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Oxford ; New York
• Pages/Volumes: xi, 257 p.
• Size: 24 cm
• Classification:
  • LCC : QA280
  • DC20 : 519.5/5
• Subject Headings:
  • LCSH : Time-series analysis
  • LCSH : Biometry -- Statistical methods
• Parent Bibliography ID:
  • BA00300551
  • BA00281843