BOOK SERIES


Chapman & Hall/CRC Monographs on Statistics & Applied Probability


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Asymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems

Asymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems

Forthcoming

Jiming Jiang
July 10, 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....

Absolute Risk: Methods and Applications in Clinical Management and Public Health

Absolute Risk: Methods and Applications in Clinical Management and Public Health

Forthcoming

Ruth M. Pfeiffer, Mitchell H. Gail
July 03, 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...

Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood, Second Edition

Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood, Second Edition

Forthcoming

Youngjo Lee, John A. Nelder, Yudi Pawitan
May 18, 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...

Multi-State Survival Models for Interval-Censored Data

Multi-State Survival Models for Interval-Censored Data

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...

Joint Modeling of Longitudinal and Time-to-Event Data

Joint Modeling of Longitudinal and Time-to-Event Data

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...

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

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

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...

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

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

Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, A. Alexandre Trindade
March 23, 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...

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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