BOOK SERIES


Chapman & Hall/CRC Monographs on Statistics & Applied Probability


119 Series Titles

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The Statistical Analysis of Multivariate Time

The Statistical Analysis of Multivariate Time

Forthcoming

Ross L. Prentice, Shanshan Zhao
August 15, 2016

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
July 15, 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

Multivariate Geostatistical Models: Inference and Computation

Multivariate Geostatistical Models: Inference and Computation

Forthcoming

Hao Zhang
July 15, 2016

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
July 15, 2016

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

Handbook of Approximate Bayesian Computation

Handbook of Approximate Bayesian Computation

Forthcoming

Scott A. Sisson, Yanan Fan, Mark Beaumont
June 30, 2016

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,

Multi-State Survival Models for Interval-Censored Data

Multi-State Survival Models for Interval-Censored Data

Forthcoming

Ardo van den Hout
May 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

Absolute or Crude Risk: Applications in Disease Prevention

Absolute or Crude Risk: Applications in Disease Prevention

Forthcoming

Mitchell H. Gail, Ruth Pfeiffer
April 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

Perfect Simulation

Perfect Simulation

Forthcoming

Mark L. Huber
November 20, 2015

Perfect simulation methods, also known as perfect sampling methods, are algorithms that draw random variates from complex physical and statistical models. These methods are used in MCMC simulation to create samples exactly from the target distribution, giving more accurate results. This book is

Inferential Models: Reasoning with Uncertainty

Inferential Models: Reasoning with Uncertainty

Forthcoming

Ryan Martin, Chuanhai Liu
October 30, 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

Semialgebraic Statistics and Latent Tree Models

Semialgebraic Statistics and Latent Tree Models

Piotr Zwiernik
August 21, 2015

Semialgebraic Statistics and Latent Tree Models explains how to analyze statistical models with hidden (latent) variables. It takes a systematic, geometric approach to studying the semialgebraic structure of latent tree models. The first part of the book gives a general introduction to key

Models for Dependent Time Series

Models for Dependent Time Series

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

Measuring Statistical Evidence Using Relative Belief

Measuring Statistical Evidence Using Relative Belief

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

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