BOOK SERIES


Chapman & Hall/CRC Monographs on Statistics & Applied Probability


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Multivariate Geostatistical Models: Inference and Computation

Multivariate Geostatistical Models: Inference and Computation

Forthcoming

Hao Zhang
May 15, 2017

Multivariate geostatistical data involves the observation of two or more spatial processes at spatial and/or temporal points. The objective of such analysis is to estimate the correlation or dependence structure between the multiple variable or to predict the multiple variable at unsampled...

Structural Nonparametric Models for the Analysis of Longitudinal Data

Structural Nonparametric Models for the Analysis of Longitudinal Data

Forthcoming

Colin O. Wu, Xin Tian
April 15, 2017

This book covers the recent advancement of statistical methods for the analysis of longitudinal data. Real datasets from four large NIH-supported longitudinal clinical trials and epidemiological studies illustrate the practical applications of the statistical methods. This book focuses on the...

Mixed Effects Model Asymptotics

Mixed Effects Model Asymptotics

Forthcoming

Jiming Jiang
February 15, 2017

Mixed effects models, including linear mixed models, generalized linear mixed models, nonlinear mixed effects models, and non-parametric mixed effects models, are complex models by nature; yet, these models are extensively used in practice. This monograph provides a comprehensive overview of...

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...

Absolute or Crude Risk: Applications in Disease Prevention

Absolute or Crude Risk: Applications in Disease Prevention

Forthcoming

Mitchell H. Gail, Ruth Pfeiffer
January 15, 2017

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...

Mathematical Foundations of Bayesian Statistics

Mathematical Foundations of Bayesian Statistics

Forthcoming

Sumio Watanabe
October 15, 2016

This book introduces the mathematical foundation of Bayesian statistics. It is well known that Bayesian inference is more accurate than the maximum likelihood method in many real-world problems: however, its mathematical foundations have been left unexplained. Recently, new research on Bayesian...

Multi-State Survival Models for Interval-Censored Data

Multi-State Survival Models for Interval-Censored Data

Forthcoming

Ardo van den Hout
August 19, 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...

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 01, 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...

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
June 29, 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,...

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