BOOK SERIES


Chapman & Hall/CRC Monographs on Statistics & Applied Probability


122 Series Titles

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Likelihood and its Extensions

Likelihood and its Extensions

Forthcoming

Nancy Reid, Cristiano Varin, Grace Y. Yi
January 26, 2017

Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with...

Handbook of Approximate Bayesian Computation

Handbook of Approximate Bayesian Computation

Forthcoming

Scott A. Sisson, Yanan Fan, Mark Beaumont
January 15, 2017

Approximate Bayesian Computation (ABC) methods, also known as "likelihood-free" inference methods, can be used to solve very complex ("computationally intractable") problems. A huge amount of research has been conducted in this area over the last decade and a varied suite of algorithms, models,...

Mixed Models

Mixed Models

Forthcoming

Geert Verbeke, Geert Molenberghs
January 15, 2017

Research on mixed models has been extensive over the most recent decade. This book differs from the authors' previous monographs on longitudinal data in that it focuses on mixed models of a linear, generalized linear and nonlinear type. The book pays attention to recent developments that include...

Statistical Learning for High-Dimensional Data

Statistical Learning for High-Dimensional Data

Forthcoming

Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou
January 15, 2017

This book gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. Offering more details on the topics than similar books, it is a valuable reference for researchers...

Covariance Modelling

Covariance Modelling

Forthcoming

Gilbert Mackenzie
December 15, 2016

Written by a researcher at the forefront of the field, this book expounds on modern theory of covariance modelling in which regression models are used to model the covariance structure simultaneously with the mean. In a systematic treatment, quite possibly the first available in a convenient format...

Absolute or Crude Risk: Applications in Disease Prevention

Absolute or Crude Risk: Applications in Disease Prevention

Forthcoming

Mitchell H. Gail, Ruth Pfeiffer
September 15, 2016

Absolute risk is the probability of developing a specific disease over a specified time interval in the presence of competing causes of mortality. Although absolute risk is arguably more relevant to clinical decision making than "pure" risk, the development of appropriate statistical methods for...

Joint Modeling of Longitudinal and Time-to-Event Data

Joint Modeling of Longitudinal and Time-to-Event Data

Forthcoming

Robert Elashoff, Gang li, Ning Li
August 30, 2016

Longitudinal data analysis and survival analysis are among the fastest expanding areas of statistics and biostatistics in the past thirty years. There has been a rapidly growing interest in joint models of longitudinal and survival data. This book is the first to give a comprehensive account of the...

Multi-State Survival Models for Interval-Censored Data

Multi-State Survival Models for Interval-Censored Data

Forthcoming

Ardo van den Hout
August 15, 2016

Multi-state models describe stochastic processes that consist of transitions between states over time, such as the three-state illness-death model. Interval-censored data is extremely common as the exact time of transition from one state to another is unknown—only an interval of time is known. This...

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition

Forthcoming

Walter Zucchini, Iain L. MacDonald, Roland Langrock
July 25, 2016

Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition. The second edition of this popular reference on the topic has been substantially revised, notably with chapter 8, extensions of the basic HMM,...

State-Space Methods for Time Series Analysis: Theory, Applications and Software

State-Space Methods for Time Series Analysis: Theory, Applications and Software

Forthcoming

Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, A. Alexandre Trindade
March 25, 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,...

Perfect Simulation

Perfect Simulation

Mark L. Huber
November 19, 2015

Exact sampling, specifically coupling from the past (CFTP), allows users to sample exactly from the stationary distribution of a Markov chain. During its nearly 20 years of existence, exact sampling has evolved into perfect simulation, which enables high-dimensional simulation from interacting...

Inferential Models: Reasoning with Uncertainty

Inferential Models: Reasoning with Uncertainty

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...

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