Chapman & Hall/CRC Monographs on Statistics and Applied Probability
About the Book 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.
Sequential Analysis: Hypothesis Testing and Changepoint Detection
1st Edition
By Alexander Tartakovsky, Igor Nikiforov, Michele Basseville
December 18, 2020
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 ...
Statistical Learning with Sparsity: The Lasso and Generalizations
1st Edition
By Trevor Hastie, Robert Tibshirani, Martin Wainwright
December 18, 2020
Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents ...
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications
1st Edition
By Vidyadhar S. Mandrekar, Leszek Gawarecki
December 18, 2020
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs).The book begins with ...
Sufficient Dimension Reduction: Methods and Applications with R
1st Edition
By Bing Li
December 18, 2020
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...
The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach
1st Edition
By Ross L. Prentice, Shanshan Zhao
December 18, 2020
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 ...
Absolute Risk: Methods and Applications in Clinical Management and Public Health
1st Edition
By Ruth M. Pfeiffer, Mitchell H. Gail
September 30, 2020
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 ...
Analysis of Repeated Measures
1st Edition
By Martin J. Crowder, David J. Hand
September 30, 2020
Repeated measures data arise when the same characteristic is measured on each case or subject at several times or under several conditions. There is a multitude of techniques available for analysing such data and in the past this has led to some confusion. This book describes the whole spectrum of ...
Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data
1st Edition
By Paul Gustafson
June 30, 2020
Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years ...
Circular and Linear Regression: Fitting Circles and Lines by Least Squares
1st Edition
By Nikolai Chernov
June 30, 2020
Find the right algorithm for your image processing applicationExploring the recent achievements that have occurred since the mid-1990s, Circular and Linear Regression: Fitting Circles and Lines by Least Squares explains how to use modern algorithms to fit geometric contours (circles and circular ...
Constrained Principal Component Analysis and Related Techniques
1st Edition
By Yoshio Takane
June 30, 2020
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 way? ...
Cyclic and Computer Generated Designs
2nd Edition
By J.A. John, E.R. Williams
June 30, 2020
Cyclic and Computer Generated Designs is a much-expanded and updated version of the well-received monograph, Cyclic Designs . The book is primarily concerned with the construction and analysis of designs with a number of different blocking structures, such as revolvable designs, row-column designs,...
Joint Modeling of Longitudinal and Time-to-Event Data
1st Edition
By Robert Elashoff, Gang li, Ning Li
June 30, 2020
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 ...