Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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1 - 12 of 117 Series Titles

Multi-State Survival Models for Interval-Censored Data

Forthcoming

Ardo van den Hout

March 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 

Mixed Models

Forthcoming

Geert Verbeke, Geert Molenberghs

January 15, 2016


Structural Nonparametric Models for the Analysis of Longitudinal Data

Forthcoming

Colin O. Wu, Xin Tian

January 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 

Change-point Methodology and Applications

Forthcoming

Tze Leung Lai, Haipeng Xing

January 15, 2016


Absolute or Crude Risk: Applications in Disease Prevention

Forthcoming

Mitchell H. Gail, Ruth Pfeiffer

December 15, 2015

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

Forthcoming

Mark L. Huber

November 15, 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

Forthcoming

Chuanhai Liu, Ryan Martin

October 15, 2015

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 

Latent Tree Graphical Models: A Geometric Perspective

Forthcoming

Piotr Zwiernik

October 15, 2015

This book introduces algebraic, combinatorial, and geometric methods for hidden tree models. It presents a focused introduction to some concepts in algebraic statistics and shows how the methods can be applied in statistics. The book also gives a broad overview of the current research on hidden 

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications

Forthcoming

Vidyadhar S. Mandrekar, Leszek Gawarecki

July 02, 2015

This book discusses y the problem of Skorokhod integral, Malliavin derivative, Filtering, Equivalence and Singularity of Gaussian measures, Markov random fields, and Dynkin Isomorphism Theorem and its converse. The book covers the theory of reproducing kernel Hilbert space. The book derives 

Statistical Learning with Sparsity: The Lasso and Generalizations

Forthcoming

Trevor Hastie, Robert Tibshirani, Martin Wainwright

June 30, 2015

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 

Measuring Statistical Evidence Using Relative Belief

Forthcoming

Michael Evans

June 22, 2015

Most approaches to statistical inference refer to the evidence of something being true or false. But no theory exists that defines what this evidence is as an explicit quantity, which can lead to a lack of confidence in the conclusions. This book presents a theory of statistical inference based on 

Models for Dependent Time Series

Forthcoming

Granville Tunnicliffe Wilson, Marco Reale, John Haywood

June 05, 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 

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