Forthcoming

Scott A. Sisson, Yanan Fan, Mark Beaumont

June 30, 2016

Approximate Bayesian Computation (ABC) methods, also known as "likelihood-free" inference methods, can be used to solve very complex ("computationally intractable") problems. A huge amount of research has been conducted in this area over the last decade and a varied suite of algorithms, models, …

Forthcoming

David Stephens

June 15, 2016

This book provides a good balance of propensity score modeling theory (particularly semiparametric methods) and applications through worked examples and software, including R and Stata code. Key topics covered include longitudinal data, survival data, survey sampling, model selection, and Bayesian …

Forthcoming

Gabor J. Szekely, Maria L. Rizzo

April 26, 2016

Forthcoming

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 …

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 …

Forthcoming

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 …

Forthcoming

Mark L. Huber

December 08, 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 …

Forthcoming

Ryan Martin, Chuanhai Liu

October 30, 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 …

Forthcoming

Piotr Zwiernik

September 01, 2015

Semialgebraic Statistics and Latent Tree Models explains how to analyze statistical models with hidden (latent) variables. It takes a systematic, geometric approach to studying the semialgebraic structure of latent tree models. The first part of the book gives a general introduction to key …

Forthcoming

Granville Tunnicliffe Wilson, Marco Reale, John Haywood

August 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 …

Michael Evans

June 23, 2015

A Sound Basis for the Theory of Statistical Inference Measuring Statistical Evidence Using Relative Belief provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to measure statistical …

Vidyadhar S. Mandrekar, Leszek Gawarecki

June 23, 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 …