Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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1 - 12 of 121 Series Titles

E-Statistics: The Potential Energy of Data


Gabor J. Szekely, Maria L. Rizzo

April 26, 2016

Martingale Methods in Statistics


Yoichi Nishiyama

April 15, 2016

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 

Multi-State Survival Models for Interval-Censored Data


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 

Absolute or Crude Risk: Applications in Disease Prevention


Mitchell H. Gail, Ruth Pfeiffer

February 15, 2016

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 

Mixed Models


Geert Verbeke, Geert Molenberghs

January 15, 2016

Research on mixed models has been extensive over the most recent decade. This book differs from the authors' previous monographs on longitudinal data in that it focuses on mixed models of a linear, generalized linear and nonlinear type. The book pays attention to recent developments that include 

Structural Nonparametric Models for the Analysis of Longitudinal Data


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


Tze Leung Lai, Haipeng Xing

January 15, 2016

Perfect Simulation


Mark L. Huber

November 30, 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


Chuanhai Liu, Ryan Martin

November 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 

Semialgebraic Statistics and Latent Tree Models


Piotr Zwiernik

September 01, 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 

Models for Dependent Time Series


Granville Tunnicliffe Wilson, Marco Reale, John Haywood

July 17, 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 

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications


Vidyadhar S. Mandrekar, Leszek Gawarecki

July 02, 2015

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 

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