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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Antedependence Models for Longitudinal Data
Simultaneous Inference in Regression
Inferential Models Reasoning with Uncertainty
Models for Dependent Time Series
By Sam Efromovich
March 12, 2018
This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, ...
By Walter Zucchini, Iain L. MacDonald, Roland Langrock
June 07, 2016
Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model ...
By Jiming Jiang
June 08, 2017
Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are extensively used in practice....
By Ruth M. Pfeiffer, Mitchell H. Gail
July 26, 2017
Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients, devising public health strategies, and clinical management. The book provides sufficient technical detail to allow ...
By Dale L. Zimmerman, Vicente A. Núñez-Antón
June 14, 2017
The First Book Dedicated to This Class of Longitudinal Models Although antedependence models are particularly useful for modeling longitudinal data that exhibit serial correlation, few books adequately cover these models. By gathering results scattered throughout the literature, Antedependence ...
By Wei Liu
June 13, 2017
Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods of parameter estimation and hypothesis testing. Simultaneous Inference in Regression provides a thorough overview of the construction methods and applications of ...
By Ardo van den Hout
December 02, 2016
Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book ...
By Pierre Del Moral
October 26, 2016
In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to ...
By Robert Elashoff, Gang li, Ning Li
August 24, 2016
Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival ...
By Ryan Martin, Chuanhai Liu
September 25, 2015
A New Approach to Sound Statistical Reasoning Inferential Models: Reasoning with Uncertainty introduces the authors’ recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior ...
By Granville Tunnicliffe Wilson, Marco Reale, John Haywood
July 29, 2015
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and ...
By Michael Evans
June 23, 2015
A Sound Basis for the Theory of Statistical Inference Measuring Statistical Evidence Using Relative Belief provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to measure statistical ...