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.
FREE Two-Day Shipping for students on millions of items. Learn more |
| |||||||||||||||
Book Description
Editorial Reviews
Review
'Professor Diggle's writing is clear and to the point. This book is easy to read regardless of prior time series experience ... excellent addition to the time series literature in that it focuses on the analysis of real biomedical data.' Scott L. Zeger, School of Hygiene and Public Health, Johns Hopkins University, Statistics in Medicine 10:3
'a welcome attempt to provide an introductory account with a strong biological and medical flavour ... This modestly priced and well-written paperback must be a strong competitor as an introductory text to time series analysis whether for class use or for private reading.' Paul Davies, University of Birmingham, Royal Statistical Society News and Notes, October 1991
'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
'a welcome attempt to provide an introductory account with a strong biological and medical flavour ... This modestly priced and well-written paperback must be a strong competitor as an introductory text to time series analysis whether for class use or for private reading.' Paul Davies, University of Birmingham, Royal Statistical Society News and Notes, October 1991
'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
About the Author
Peter J. Diggle is at University of Lancaster.
Product Details
Would you like to update product info or give feedback on images?
|
More About the Author
Discover books, learn about writers, read author blogs, and more.
Customer Reviews
|
Share your thoughts with other customers:
|
||||||||||||||||||||||
|
Most Helpful Customer Reviews
26 of 26 people found the following review helpful:
4.0 out of 5 stars
nice book,
By
This review is from: Time Series: A Biostatistical Introduction (Oxford Statistical Science Series, No. 5) (Oxford Science Publications) (Paperback)
Diggle covers most of the standard approaches to time series analysis in both the time and frequency domains. The first two chapters provide a gentle introduction with the important theory covered in Chapters 3 and 4. What makes it different is the treatment of repeated measures data which comes up in clinical trials and other forms of medical research. The data are time series since they are collected over time. However in engineering and the physical sciences, it is common to deal with the analysis of a single but long time series. In the medical field the time series is often a series of repeated measurements on a patient taken over a short amount of time. Typically, the series may consist of only 3 to 5 time points. However inference is still possible because there are many patients being studied under similar conditions. So whereas in the engineering applications we only have a partial (although long) realization of a single time series in biomedical applications we have many partial (short) realizations of many time series. Some of the methods of analysis are therefore different. The approach to repeated measures is given in Chapter 5 which is authoritative and useful but does not do justice to the subject. Fortunately Diggle, Zeger and Liang have written an entire book on longitudinal analysis that came out in 1994, four years after this book. Other good books have also been written subsequently.
The latter chapters deal with case studies, model fitting and diagnostic checking and an interesting chapter on bivariate time series analysis.
Share your thoughts with other customers: Create your own review
|
|
Tags Customers Associate with This Product(What's this?)Click on a tag to find related items, discussions, and people.
|
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
Customer Discussions
|
This product's forum
Active discussions in related forums
Search Customer Discussions
|
Related forums
|
