Exercises designed to strengthen creative problem-solving skills, designed to encourage readers to understand the key points of a problem while seeking knowledge, rather than separating out these two activities. To complete the exercises, readers ma...Daugiau...
Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to g...Daugiau...
Like the De Groot winning first edition, the second edition of Principles of Uncertainty is an accessible, comprehensive guide to the theory of Bayesian Statistics written in an appealing, inviting style, and packed with interesting examples....Daugiau...
This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. The book is based on lecture notes prepared by the first author, subsequently edited, expanded and updated by the second author. Includ...Daugiau...
Links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems, it shows how recent advances in methodology can assess the health risks associated with environmental hazards. The books clear gui...Daugiau...
Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and pu...Daugiau...
This books is meant for a standard one-semester advanced undergraduate or graduate level course on Mathematical Statistics. It covers all the key topics - statistical models, linear normal models, exponential families, estimation, asymptotics of max...Daugiau...
Statistical Methods for Spatial Data Analysis is a comprehensive treatment of statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. The authors deliver an outst...Daugiau...
Incorporating a collection of recent results, Pólya Urn Models deals with discrete probability through the modern and evolving urn theory and its numerous applications. It looks at how some classical problems of discrete probability have roots in ur...Daugiau...
Designed for a one-semester advanced undergraduate or graduate statistical theory course, this book clearly explains the underlying ideas, mathematics, and principles of major statistical concepts, including parameter estimation, confidence interval...Daugiau...
This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing...Daugiau...
Practical Time Series Analysis for Data Science is an accessible guide that doesnt require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed....Daugiau...
It exposes students to the foundations of classical experimental design and observational studies through a modern framework. A causal inference framework is important in design, data collection and analysis since it provides a framework for investi...Daugiau...
Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes....Daugiau...
The purpose of applying mathematical theory to the theory of statistical inference is to make it simpler and more elegant. Theory of Statistical Inference is concerned with the development of a type of optimization theory which can be used to inform...Daugiau...
Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied s...Daugiau...
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 vi...Daugiau...
This book is a first course in probability and statistics using R. The book assumes a mathematical background of Calculus II, though much of the book can be read with a much lower level of mathematics. The book incorporates R throughout all sections...Daugiau...
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....Daugiau...
This book takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results....Daugiau...
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 nonpar...Daugiau...
Building on the authors more than 35 years of teaching experience, Modeling and Analysis of Stochastic Systems, Third Edition, covers the most important classes of stochastic processes used in the modeling of diverse systems. For each class of stoch...Daugiau...
This text develops students professional skills in statistics with applications in finance. It bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time se...Daugiau...
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 ab...Daugiau...
This book introduces the use of statistical concepts and methods to model and analyze financial data, including the market model, the single-index model, and factor models. It contains detailed numerical examples using genuine financial data along w...Daugiau...
Categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. This title presents various ways of extracting real-life conclusions from contingency tables. It uses a Fisherian approach...Daugiau...
This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kern...Daugiau...
Using linear statistical models as a basis for statistical inference and the theoretical underpinnings of resultant inferential procedures. Includes topics typically covered less extensively; prediction, multiple-comparison procedures for controllin...Daugiau...
This book synthesizes those techniques from numerical analysis, algorithms, data structures, and optimization theory mostcommonly employed in statistics and machine learning. We provide concrete applications of these methods by giving complete refer...Daugiau...
Surrogates is a graduate textbook, on topics at the interface between machine learning,spatial statistics,computer simulation,meta-modeling,design of experiments,and optimization. Experimentation through simulation,management of dynamic processes,on...Daugiau...
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...Daugiau...
This book covers experimental design and the more advanced techniques of data analysis which can be used to assess performance, to compare methods and to control quality. It is useful to anyone concerned with the development or the management of com...Daugiau...
This book is about practical forecasting and analysis of time series. It describes how to analyse time series data, how to identify structure, how to explain observed behaviour, how to model structures and behaviours, and how to use insight gained f...Daugiau...
This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences, engineering, and physical, social and statistical sciences. It also introduces the topic of stochastic...Daugiau...
This handbook aims to demonstrate the flexibility of Genstat by analysing the wide range of examples discussed in Applied Statistics: Principles and Examples. It shows how to reproduce the tables and results discussed in Applied Statistics....Daugiau...
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...Daugiau...
This book presents a concise introduction to the theory underlying the analysis of the main types of spatial data. It includes examples to illustrate the topics, including R code for their implementation, as well as exercises to support course teach...Daugiau...
A practical guide to statistical methods which reflects developments in the field. It includes a chapter introducing mixed models for binary data analysis and another on methods for modelling binary data. It also includes material on modelling order...Daugiau...
Covers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. This work provides the option o...Daugiau...
This book teaches students how to apply nonparametric techniques to statistical data from hypotheses to regression modeling, time-to-event analysis, density estimation, and resampling methods. SAS codes for all examples are given in the text. Data s...Daugiau...
Focusing on the important role that statistical methods play in the analysis of the data collected as well as in the overall clinical trial process, this title provides an introduction to clinical trials. It features examples, exercises, and materia...Daugiau...
Employing non-full-rank design matrices throughout, this book enables understanding of basic linear models. This book introduces the basic algebra and geometry of the linear least squares problem, before delving into estimability and the Gauss-Marko...Daugiau...
Suitable for students of survival analysis and reliability, this textbook integrates the use of contemporary statistical software output throughout the text along with a variety of real data sets so readers can see how theories and techniques are ap...Daugiau...
