Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

Series Editor(s): Richard L. Smith, Valerie Isham, Howell Tong, Niels Keiding, Thomas A. Louis, Florentina Bunea, Jianqing Fan



Viewing: 1 - 25 of 116
Published:
July 15, 2015
Author(s):
Chuanhai Liu, Ryan Martin
This book delves into the authors’ work toward deeper understanding of statistical inference in terms of reasoning with uncertainty and meaningfulness of probabilistic inferential output. Focusing on a valid, prior-free probabilistic inferential framework called inferential models, the authors 
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Published:
July 15, 2015
Author(s):
Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou
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 
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Published:
July 15, 2015
Author(s):
Colin O. Wu, Xin Tian
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 
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Published:
July 15, 2015
Author(s):
Yoichi Nishiyama
This gives a comprehensive introduction to the (standard) statistical analysis based on the theory of martingales and develops entropy methods in order to treat dependent data in the framework of martingales. The author starts a summary of the martingale theory, and then proceeds to give full 
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Published:
June 26, 2015
Author(s):
Gilbert Mackenzie
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 
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Published:
June 26, 2015
Author(s):
David Stephens

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Published:
June 15, 2015
Author(s):
Ardo van den Hout
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 
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Published:
May 26, 2015
Author(s):
Victor Patrangenaru, Lief Ellingson
Requiring only a background in mathematical statistics or analysis, this book provides the first comprehensive look at the statistics of manifolds. It presents location and spread parameters for distribution on manifolds, followed by key large sample theory results. The text also addresses 
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Published:
March 26, 2015
Author(s):
Barry C. Arnold

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Published:
March 15, 2015
Author(s):
Marco Reale, Granville Tunnicliffe-Wilson, John Haywood
Authored by leading researchers in multivariate time series, this book introduces time series models based on lagged regression, particularly vector autoregressive models. It covers recent developments, such as graphical modeling and generalized lag models, and extends them to the important case of 
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Published:
January 26, 2015
Author(s):
Paul Gustafson
Many observational studies in epidemiology and other disciplines face inherent limitations in study design and data quality, such as selection bias, unobserved variables, and poorly measured variables. Accessible to statisticians and researchers from various disciplines, this book presents an 
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Published:
December 26, 2014
Author(s):
Christophe Giraud
This book provides a straightforward, up-to-date introduction to high-dimensional statistics. It avoids any unnecessary technicalities, instead focusing on the main underlying concepts in simple settings. It gives readers a strong background in high-dimensional statistics by explaining key concepts 
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Published:
October 06, 2014
Author(s):
Byron Jones, Michael G. Kenward
Since the publication of the second edition, there has been significant research and development in cross-over trials, which continue to be an important design technique in the pharmaceutical industry. This new edition has been updated with recent references and new material on analysis of small 
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Published:
September 02, 2014
Author(s):
Gunter Ritter
Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications 
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Published:
August 19, 2014
Author(s):
Alexander Tartakovsky, Igor Nikiforov, Michele Basseville
Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent 
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Published:
August 07, 2014
Author(s):
Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand
Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling 
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Published:
July 07, 2014
Author(s):
Patrick Laurie Davies
The First Detailed Account of Statistical Analysis That Treats Models as Approximations The idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist 
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Published:
June 26, 2014
Author(s):
Harry Joe
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine 
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Published:
January 28, 2014
Author(s):
Justine Shults, Joseph M. Hilbe
Drawing on the authors’ substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression—a computational approach for the estimation of correlation parameters within the framework of generalized 
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Published:
December 26, 2013
Author(s):
Anthony C Atkinson, Atanu Biswas
Randomised Response-Adaptive Designs in Clinical Trials presents methods for the randomised allocation of treatments to patients in sequential clinical trials. Emphasizing the practical application of clinical trial designs, the book is designed for medical and applied statisticians, clinicians, 
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Published:
December 21, 2013
Author(s):
Ching-Shui Cheng
Bringing together both new and old results, Theory of Factorial Design: Single- and Multi-Stratum Experiments provides a rigorous, systematic, and up-to-date treatment of the theoretical aspects of factorial design. To prepare readers for a general theory, the author first presents a unified 
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Published:
October 24, 2013
Author(s):
Yoshio Takane
In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial 
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Published:
July 23, 2013
Author(s):
Peter J. Diggle
Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical 
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Published:
June 18, 2013
Author(s):
Jin-Ting Zhang
Despite research interest in functional data analysis in the last three decades, few books are available on the subject. Filling this gap, Analysis of Variance for Functional Data presents up-to-date hypothesis testing methods for functional data analysis. The book covers the reconstruction of 
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Published:
May 20, 2013
Author(s):
Pierre Del Moral
In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to 
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