Complex Data Modeling and Computationally Intensive Statistical Methods

Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of stu

This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bay

A guide to achieving business successes through statistical methodsStatistical methods are a key ingredient in providing data-based guidance to research and development as well as to manufacturing. Un

This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable.It features the most current methods on modeling repeated measures data as

For more than 100 years, Henry's Clinical Diagnosis and Management by Laboratory Methods has been recognized as the premier text in clinical laboratory medicine, widely used by both clinical pathologists and laboratory technicians. Leading experts in each testing discipline clearly explain procedures and how they are used both to formulate clinical diagnoses and to plan patient medical care and long-term management. Employing a multidisciplinary approach, it provides cutting-edge coverage of automation, informatics, molecular diagnostics, proteomics, laboratory management, and quality control, emphasizing new testing methodologies throughout.Remains the most comprehensive and authoritative text on every aspect of the clinical laboratory and the scientific foundation and clinical application of today's complete range of laboratory tests. Updates include current hot topics and advances in clinical laboratory practices, including new and extended applications to diagnosis and management.

A fascinating look at the treatment of depression, blending journalism, science, history, and memoir, by an award-winning science writer.What is depression? Is it a persistent low mood or a complex ra

This book is a standard for a complete description of the methods for unconstrained optimization and the solution ofnonlinear equations....this republication is most welcome and this volume should be

In today’s world, numbers are in the ascendancy. Societies dominated by star ratings, scores, likes and lists are rapidly emerging, as data are collected on virtually every aspect of our lives. From a

Based on rare first-hand historical data, Wang Ke presents the analysis of the East Turkestan from the perspective of Islamic social structure, the origin and evolution of thoughts on national revolut

As a vehicle to convey both the history of modern China and the complex forces still driving the nation’s economic success, rail has no equal. Railroads and the Transformation of China is the first co

Facebook and Conversation Analysis investigates the structure and organization of comments on a major social media platform, Facebook, using applied conversation analysis methods. Providing previously

'stunning...succeeds in exploring complex topics in an exciting and visually accessible way' Born to Engineer 'smart writing and wonderful illustrations' How it Works Plunge into the world of science

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"The author provides unprecedented critical, interdisciplinary explorations of the complex dynamics and intersectionalities operating between disability and poverty in rural areas, an assumed relationship that is too often misunderstood. Reporting on long term ethnographic work in Guatemala and prioritising the voices, knowledge and narratives of disabled people and their families, Grech offers an incisive and refined critical analysis of the various complex barriers and interactions in the disability/poverty/rurality nexus encountered by what Grech calls 'disabled families'. The book opens up discussions on a broad range of themes traversing conceptual, spatial, historical, embodied, spiritual, racial, sexual and gendered terrains among others. It challenges and reframes established, often imposed discourses and practices, and contests issues of (re)presentation, Eurocentrism, and coloniality. Finally, it contributes new and uncharted reflections for further debate, which are indispen

A new title in Young Reading, Series Three. Aimed at children whose reading ability and confidence allows them to tackle longer and more complex stories. A fascinating look at The Second World War and

This is a complete guide to the pricing and risk management of convertible bond portfolios. Convertible bonds can be complex because they have both equity and debt like features and new

The classic swashbuckling adventure by Alexander Dumas retold for children ready to tackle longer and more complex stories. D'Artagnan longed to be one of that elite band of fighters, the King's muske

The classic swashbuckling adventure by Anthony Hope retold for children ready to tackle longer and more complex stories. When Rudolf Rassendyll, a young Victorian gentleman, travels to Ruritania, all

This is a new title in "Young Reading Series Two", which is aimed at children whose reading ability and confidence allows them to tackle longer and more complex stories. It tells the story of Diwali,

This is a new title in "Young Reading Series Three", which is aimed at children whose reading ability and confidence allows them to tackle longer and more complex stories. It tells the life story of t

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

A comprehensive introduction to the most commonly used statistical methods relevant in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, and illustrated with concrete examples, with relevant statistical and scientific concepts explained as needed. Particular attention is paid to nuances and pitfalls, with sufficient detail to enable the reader to write relevant code. Topics covered include hypothesis testing, time series analysis, linear regression, data assimilation, extreme value analysis, Principal Component Analysis, Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. The specific statistical challenges that arise in climate applications are also discussed, including model selection problems associated with Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. Requiring no previous background in statistics, this is a highly accessibl

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with po

Master data analysis, modeling and the effective use of spreadsheets with the popular BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 7E. The quantitative methods approach in this edition helps

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to

The quality of the integration of multiple data sources is key to ensuring that the data can be properly analyzed. This book presents statistical methods for analyzing integrated data, covering uncert

This book provides new theories, applications and quantitative methods in demography, population studies and statistics. It presents and applies data analysis, statistics and stochastic modeling techn

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostat

Presenting in depth discussions of various statistical models and methods needed for the analysis of medical, biological and biostatistics data, this book emphasizes the analysis of data when normal d

This book explores outcome modeling in cancer from a data-centric perspective to enable a better understanding of complex treatment response, to guide the design of advanced clinical trials, and to ai

What factors will influence the shipping market, and how are they linked? By studying the current and historical market, we are going to discover some new links. This book's charts and historical data

This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines su

For courses in Statistical Methods for the Social Sciences . Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for t

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction

Designed for researchers and data analysts, this book presents rigorous statistical methods without burdening readers with mathematical theory. Readers see how linear regression and ANOVA can be used

The book provides a concise introduction into inverse modeling, i.e the theory and methods of inverse problems and data assimilation.Inverse problems are widely spread today in science and technology,

Multiphysics Modeling: Numerical Methods and Engineering Applications: Tsinghua University Press Computational Mechanics Series describes the basic principles and methods for multiphysics modeling, co

This sophisticated package of statistical methods is for advanced master's (MPH) and PhD students in public health and epidemiology who are involved in the analysis of data. It makes the link from statistical theory to data analysis, focusing on the methods and data types most common in public health and related fields. Like most toolboxes, the statistical tools in this book are organized into sections with similar objectives. Unlike most toolboxes, however, these tools are accompanied by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls - conveying skills, intuition, and experience. The only prerequisite is a first-year statistics course and familiarity with a computing package such as R, Stata, SPSS, or SAS. Though the book is not tied to a particular computing language, its figures and analyses were all created using R. Relevant R code, data sets, and links to public data sets are available from www.cambridge.org/97811071130

Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified appr