Bøger i Chapman & Hall/CRC Texts in Statistical Science serien

Bayesian statistics has been advancing in many aspects in recent years. Bayesian learning provides a natural framework for students to solve scientific problems. This book provides an introduction to Bayesian analysis for undergraduate students with calculus, statistics, and a computational background.

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

Covers aspects of probability, distribution theory and random processes that are fundamental to a proper understanding of inference. This book discusses the properties of estimators constructed from a random sample of ends, with sections on methods for estimating parameters in time series models.

Designed to provide a good balance of theory and computational methods that will appeal to students and practitioners with minimal mathematical and statistical background and no experience in Bayesian statistics to students and practitioners looking for advanced methodologies.

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.

Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include:　 All examples based on work in industry, consulting to industry, and research for industry　 Examples and case studies include all engineering disciplines Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions Intuitive explanations are followed by succinct mathematical justifications Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference Use of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applications Use of multiple regression for times series models and analysis of factorial and central composite designs　 Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooks Experiments designed to show fundamental concepts that have been tested with large classes working in small groups Website with additional materials that is regularly updated Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for Quality. 　 　

Based on the authors lecture notes, Introduction to the Theory of Statistical Inference presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles

This text provides a modern introduction to regression and classification with an emphasis on big data and R. The main body uses math stat sparingly and always in the context of something concrete; readers can skip the math stat content entirely if they wish.

The book provides an introduction to functional data analysis (FDA), useful to students and researchers. FDA is now generally viewed as a fundamental subfield of statistics. FDA methods have been applied to science, business and engineering.

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

Offering deep insight into the connections between design choice and the resulting statistical analysis, this text explores how experiments are designed using the language of linear statistical models. It presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models. The text describes specific forms or classes of experimental designs, incorporates actual experiments drawn from the scientific and technical literature, and includes many end-of-chapter exercises. Calculations are performed using R, with commands provided in an appendix. A solutions manual is available upon qualified course adoption.

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book¿s R data package gamair, to enable use as a course text or for self-study.

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.

This second edition focuses on modeling unbalanced data. It presents many new topics, including new chapters on logistic regression, log-linear models, and time-to-event data. It shows how to model main-effects and interactions and introduces nonparametric, lasso, and generalized additive regression models. The text carefully analyzes small unbalanced data by using tools that are easily scaled to big data. R, Minitab®, and SAS codes are available on the author¿s website.

This book defines and investigates the concept of a random object. To accomplish this task in a natural way, it brings together three major areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by existing textbooks

This text presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results. Along with describing the necessary statistical theory, the authors illustrate the practical application of the techniques to a large number of substantive problems. The data sets and R code are available on a supplementary website.

This book provides a comprehensive reference for solving scientific problems with the generalized linear geostatistical model (GLGM), with an emphasis on demonstrating the accompanying software through examples. The key features of the GLGM are observed data points being independent of each other conditional on an unobserved spatial surface, values of the underlying spatial surface follow a multivariate normal distribution, and the surface is the sum of explanatory variables (fixed effects) and a random term.

This text provides graduate students with a rigorous treatment of probability theory, with an emphasis on results central to theoretical statistics. It presents classical probability theory motivated with illustrative examples in biostatistics, such as outlier tests, monitoring clinical trials, and using adaptive methods to make design changes based on accumulating data. In addition, counterexamples further clarify nuances in meaning and expose common fallacies in logic. The authors explain different methods of proofs and show how they are useful for establishing classic probability results.

Volume I presents fundamental, classical statistical concepts at the doctorate level without using measure theory. It gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. Volume II covers a number of topics that are important in current measure theory and practice. It emphasizes nonparametric methods which can really only be implemented with modern computing power on large and complex data sets. In addition, the set includes a large number of problems with more difficult ones appearing with hints and partial solutions for the instructor.

This text presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results. R code is used to create and analyze all the example experiments, with the code examples available on the author¿s website.

This text presents a balanced account of the Bayesian and frequentist approaches to statistical inference. Along with more examples and exercises, this second edition includes new material on empirical Bayes and penalized likelihoods and their impact on regression models and offers expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models. It also compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two.

This text shows how to use multivariate analysis to extract useful information from multivariate data and understand the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification, discrimination, dimension reduction, and clustering. Many examples and figures throughout facilitate a deep understanding of the multivariate analysis techniques, including how to select the optimal model.

Like its widely praised, best-selling predecessor, this second edition explains how to use linear models in physical science, engineering, social science, and business applications. The material on interpreting linear models now distinguishes the main applications of prediction and explanation and introduces elementary notions of causality. This edition also covers QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates. It extensively uses R¿s ggplot2 graphics package in addition to base graphics.

Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis.