Oxford Statistical Science S.
2 primary works
Book 5
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.
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.
Book 13
The Analysis of Longitudinal Data
by Peter J. Diggle, etc., Kung-Yee Liang, and Scott L. Zeger
Published 14 July 1994
This book describes statistical models and methods for the analysis of longitudinal data, with a strong emphasis on applications in the biological and health sciences. The technical level of the book is roughly that of a final year undergraduate or first year postgraduate course in statistics. The book divides naturally into three blocks. The first three chapters provide an introduction to the subject, and cover basic issues of design and exploratory analysis. Chapters 4, 5, 6, and 11 develop linear models and associated statistical methods for data sets in which the response variable is a continuous measurement. Chapters 7, 8, 9, and 10 are concerned with generalized linear models for discrete response variables. Appendix A gives a brief review of the statistical background assumed. This book is intended for statisticians - MSc students and researchers.