Probability and statistics

Originally published in 1929.

The book describes the core resources in informatics necessary to support biomedical research programs and how these can best be integrated with hospital systems to receive clinical information that is necessary to conduct translational research.

A comprehensive 2010 summary of interdisciplinary social methods and research strategies for analysis of the complex relationship between environments and societies.

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences.

This book constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Algorithmic Decision Theory , ADT 2015, held in September 2015 in Lexington, USA.

Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science.

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models using SAS.

Business students need the ability to think statistically about how to deal with uncertainty and its effect on decision-making in business and management.

Winner of the 2006 Joseph W.

Focusing on recent advances in option pricing under the SABR model, this book shows how to price options under this model in an arbitrage-free, theoretically consistent manner.

"e;We live in the age of data.

This book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests.

This monograph discusses recent advances in ergodic theory and dynamical systems.

This book provides a self-contained presentation on the structure of a large class of stable processes, known as self-similar mixed moving averages.

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field.

This book demonstrates in detail all phases of the 9th National Forest Inventory of Finland (1996-2003): the planning of the sampling design, measurements, estimation methods and results.

In 1946 Paul Halmos studied unbiased estimators of minimum variance, and planted the seed from which the subject matter of the present monograph sprang.

Emphasizing interpretation of results, this hands-on guide explains why, when, and how to use mixed models with your data.

Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods.

This book provides analytic tools to describe local and global behavior of solutions to Ito-stochastic differential equations with non-degenerate Sobolev diffusion coefficients and locally integrable drift.

This updated and revised first-course textbook in applied probability provides a contemporary and lively post-calculus introduction to the subject of probability.

This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists.

Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course.

Methods of nonlinear time series analysis are discussed from a dynamical systems perspective on the one hand, and from a statistical perspective on the other.

Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths.

As evidenced by the anthrax attacks in 2001, the SARS outbreak in 2003, and the H1N1 influenza pandemic in 2009, a pathogen does not recognize geographic or national boundaries, often leading to devastating consequences.

Written in a lecture format with solved problems at the end of each chapter, this book surveys quantitative modeling and decision analysis techniques.

Outside of randomized experiments, association does not imply causation, and yet there is nothing defective about our knowledge that smoking causes lung cancer, a conclusion reached in the absence of randomized experimentation with humans.

This book introduces text analytics as a valuable method for deriving insights from text data.

This volume contains two review articles: "e;Stochastic Pro- gramming"e; by Vo V.

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems.

This text bridges the gap between sound theoretcial developments and practical, fruitful methodology by providing solid justification for standard symptotic statistical methods.

This book enables readers to understand, model, and predict complex dynamical systems using new methods with stochastic tools.

This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers.

Non-analogue Monte Carlo methods are useful when the direct simulation techniques are insufficient.

First issued in translation as a two-volume work in 1975, this classic book provides the first complete development of the theory of probability from a subjectivist viewpoint.

Exercises and Solutions in Statistical Theory helps students and scientists obtain an in-depth understanding of statistical theory by working on and reviewing solutions to interesting and challenging exercises of practical importance.

Reliably optimizing a new treatment in humans is a critical first step in clinical evaluation since choosing a suboptimal dose or schedule may lead to failure in later trials.

Efficient and equitable policies for managing disaster risks and adapting to global environmental change are critically dependent on development of robust options supported by integrated modeling.

This book will provide scientists with a better understanding of statistics, improving their decision-making and reducing animal use.

Offering a step-by-step approach for applying the Nonparametric Method with the Bayesian Approach to model complex relationships occurring in Reliability Engineering, Quality Management, and Operations Research, it also discusses survival and censored data, accelerated lifetime tests (issues in reliability data analysis), and R codes.

Trying to read up on statistics can be like trying to decide where you want to start eating the elephant and what's the most digestible way to get it down.

Learn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python.

This book shows the capabilities of Microsoft Excel in teaching health services management statistics effectively.

The book gives a comprehensive overview of modern non-life actuarial science.