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Chapman & Hall/CRC Monographs on Statistics and Applied Probability


About the Series

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.

Please contact us if you have an idea for a book for the series.

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Components of Variance

Components of Variance

1st Edition

By D.R. Cox, P.J. Solomon
September 05, 2019

Identifying the sources and measuring the impact of haphazard variations are important in any number of research applications, from clinical trials and genetics to industrial design and psychometric testing. Only in very simple situations can such variations be represented effectively by ...

Subjective Probability Models for Lifetimes

Subjective Probability Models for Lifetimes

1st Edition

By Fabio Spizzichino
September 05, 2019

Bayesian methods in reliability cannot be fully utilized and understood without full comprehension of the essential differences that exist between frequentist probability and subjective probability. Switching from the frequentist to the subjective approach requires that some fundamental concepts be...

Subset Selection in Regression

Subset Selection in Regression

2nd Edition

By Alan Miller
September 05, 2019

Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition...

The Statistical Analysis of Multivariate Failure Time Data A Marginal Modeling Approach

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach

1st Edition

By Ross L. Prentice, Shanshan Zhao
May 16, 2019

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of ...

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

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

1st Edition

By 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, ...

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

2nd Edition

By Youngjo Lee, John A. Nelder, Yudi Pawitan
August 04, 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 ...

Large Covariance and Autocovariance Matrices

Large Covariance and Autocovariance Matrices

1st Edition

By Arup Bose, Monika Bhattacharjee
July 03, 2018

Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites ...

Nonparametric Models for Longitudinal Data With Implementation in R

Nonparametric Models for Longitudinal Data: With Implementation in R

1st Edition

By Colin O. Wu, Xin Tian
May 15, 2018

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era ...

Multistate Models for the Analysis of Life History Data

Multistate Models for the Analysis of Life History Data

1st Edition

By Richard J Cook, Jerald F. Lawless
May 04, 2018

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks...

Multivariate Kernel Smoothing and Its Applications

Multivariate Kernel Smoothing and Its Applications

1st Edition

By José E. Chacón, Tarn Duong
May 08, 2018

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite ...

Sufficient Dimension Reduction Methods and Applications with R

Sufficient Dimension Reduction: Methods and Applications with R

1st Edition

By Bing Li
May 01, 2018

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of...

Probabilistic Foundations of Statistical Network Analysis

Probabilistic Foundations of Statistical Network Analysis

1st Edition

By Harry Crane
April 19, 2018

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic ...

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