Excel 2016 for Biological and Life Sciences Statistics, Quirk
Автор: Richard A. Johnson, Gouri K. Bhattacharyya Название: Statistics: Principles and Methods, 7th Edition ISBN: 0470904119 ISBN-13(EAN): 9780470904114 Издательство: Wiley Рейтинг: Цена: 35369.00 р. Наличие на складе: Поставка под заказ.
Описание: Statistics: Principles and Methods, 7th Edition provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters include real-world data, designed to make the material more relevant. The numerous examples clearly demonstrate the important points of the methods.
Автор: Van Houwelingen Название: Dynamic Prediction in Clinical Survival Analysis ISBN: 1439835330 ISBN-13(EAN): 9781439835333 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models.
Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts:
Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model
Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated
Part III is dedicated to the use of time-dependent information in dynamic prediction
Part IV explores dynamic prediction models for survival data using genomic data
Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.
Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
New to the Second Edition
The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
New chapter on simulation that includes examples of data generated from complex models and distributions
A detailed discussion of the philosophy and use of the knitr and markdown packages for R
New packages that extend the functionality of R and facilitate sophisticated analyses
Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots
Easily Find Your Desired Task
Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Автор: Zucchini Название: Hidden Markov Models for Time Series ISBN: 1482253836 ISBN-13(EAN): 9781482253832 Издательство: Taylor&Francis Рейтинг: Цена: 14086.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition.
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