Since its inception in 1960 under the leadership of Sir David R. Cox, the series has established itself as a leading outlet for monographs presenting advances in statistical and applied probability research. With over 150 books published - over 100 still in print - the series has gained a reputation for outstanding quality.
The scope of the series is wide, incorporating developments in statistical methodology of relevance to a range of application areas. The monographs in the series present succinct and authoritative overviews of methodology, often with an emphasis on application through worked examples and software for their implementation. They are written so as to be accessible to graduate students, researchers and practitioners of statistics, as well as quantitative scientists from the many relevant areas of application.
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By D.R. Cox, Nanny Wermuth
October 17, 2019
Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences. This book sets out both the general concepts and the more technical statistical issues involved in analysis ...
By A.D. Gordon
October 07, 2019
As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification does just ...
By Vilijandas Bagdonavicius, Mikhail Nikulin
September 05, 2019
The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing models. In a unified, systematic presentation, this monograph fully details those models and explores areas of accelerated life testing usually only touched upon...
By D.R. Cox, P.J. Solomon
September 05, 2019
Identifying the sources and measuring the impact of haphazard variations are important in any number of research applications, from clinical trials and genetics to industrial design and psychometric testing. Only in very simple situations can such variations be represented effectively by ...
By Fabio Spizzichino
September 05, 2019
Bayesian methods in reliability cannot be fully utilized and understood without full comprehension of the essential differences that exist between frequentist probability and subjective probability. Switching from the frequentist to the subjective approach requires that some fundamental concepts be...
By Alan Miller
September 05, 2019
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition...
By Ross L. Prentice, Shanshan Zhao
May 14, 2019
The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of ...
By Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, A. Alexandre Trindade
March 16, 2016
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, ...
By Youngjo Lee, John A. Nelder, Yudi Pawitan
July 25, 2017
This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical ...
By Arup Bose, Monika Bhattacharjee
July 02, 2018
Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites ...
By Colin O. Wu, Xin Tian
May 10, 2018
Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era ...
By Richard J Cook, Jerald F. Lawless
May 15, 2018
Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks...