Böcker i Chapman & Hall/CRC Monographs on Statistics and Applied Probability-serien

While this book discusses a variety of approaches to prediction, including those based on parametric, nonparametric, and non-stochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach.

Since the 1930s, basic statistical problems have been formulated not in terms of interpreting data as evidence but in terms of choosing between alternative courses of action. This monograph is about the problem of representing and interpreting data as evidence.

Energy statistics are functions of distances between statistical observations in metric spaces. The authors hope this book will spark the interest of most statisticians who so far have not explored E-statistics and would like to apply these new methods using R.

Object Oriented Data Analysis (OODA) provides a useful general framework for the consideration of many types of Complex Data. It is deliberately intended to be particularly useful in the analysis of data in complicated situations which are typically not easily represented as an unconstrained matrix of numbers.

This book provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It attempts to establish a gold standard for how a statistical analysis should proceed. The book illustrates relative belief theory using many examples and describes the strengths and weaknesses of the theory. Th

Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are extensively used in practice. This monograph provides a comprehensive account

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in var

This book preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities.

This book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic

This book provides broad, up-to-date coverage of the Pareto model and its extensions. This edition expands several chapters to accommodate recent results and reflect the increased use of more computer-intensive inference procedures. It includes new material on multivariate inequality and new discussions of bivariate and multivariate income and s

In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. The authors cover the lasso for linear regression, generali

This monograph presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, the authors study Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs). They explain how covariances are related to RKHSs and examine the Bayes' formula, the filtering and analytic problem related to

This book introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The book covers the foundational motivations for this new approach, the basic theory behind its calibration properties, ma

Sufficient dimension reduction was first introduced in the early 90's as a set of graphical and diagnostic tools for regression with many predictors. Over the past two decades or so it has developed into a powerful theory and technique for handling high-dimensional data. This book will introduce the main results and important techniques in this

Though much has been written on multivariate failure time data analysis methods, a unified approach to this topic has yet to be communicated. This book aims to fill that gap through a novel focus on marginal hazard rates and cross ratio modeling. Readers will find the content useful for instruction, for application in collaborative research and

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 proofs of the martingale central limit theorems.

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 diagnostics, semi-parametric methodology, non-normal random effects, multivariate longitudinal data, high-dimensional outcomes, joint modeling of longitudinal and survival data and discrimination and classification based on longitudinal data using mixed models.

This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.

Exploring the recent achievements that have occurred since the mid-1990s, this book explains how to use modern algorithms to fit geometric contours to observed data in image processing and computer vision. The author covers all facets-geometric, statistical, and computational-of the methods. He looks at how the numerical algorithms relate to one

This book shows how constrained principal component analysis (CPCA) offers a unified framework for regression techniques and PCA. Keeping the use of complicated iterative methods to a minimum, the book includes implementation details and many real application examples. It also offers material for methodologically oriented readers interested in d

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the Data Science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges.

This book will describe a variety of statistical models useful for the analysis of data arising from life history processes. Particular attention will be paid to models useful for the study of chronic diseases to better understand the dynamics of the disease process, the effects of fixed and time-varying covariates, and the use of models for pre

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precisio

Exploring the advantages of the state-space approach, this book presents numerous computational procedures that can be applied to a previously specified linear model in state-space form. It discusses model estimation and signal extraction; describes many procedures to combine, decompose, aggregate, and disaggregate a state-space form; and covers