Applied Statistical Methods in Agriculture, Health and Life Sciences, Bayo Lawal
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.
Описание: The Manchester Physics Series General Editors: D.J. Sandiford; F. Mandl; A.C. Phillips Department of Physics and Astronomy, University of Manchester Properties of Matter B.H. Flowers and E. Mendoza Optics Second Edition F.G. Smith and J.H. Thomson Statistical Physics Second Edition F. Mandl Electromagnetism Second Edition I.S. Grant and W.R.
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: Nachtsheim;Neter;Kutner Название: Applied Linear Statistical Models with Student CD ISBN: 0071122214 ISBN-13(EAN): 9780071122214 Издательство: McGraw-Hill Рейтинг: Цена: 9265.00 р. Наличие на складе: Поставка под заказ.
Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.
Автор: Agarwal Название: Statistical Methods for Recommender Systems ISBN: 1107036070 ISBN-13(EAN): 9781107036079 Издательство: Cambridge Academ Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.
Описание: This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. It is developed for undergraduate students in agriculture, nursing, biology and biomedical research.
Автор: Jean-Louis Auget; N. Balakrishnan; Mounir Mesbah; Название: Advances in Statistical Methods for the Health Sciences ISBN: 0817643680 ISBN-13(EAN): 9780817643683 Издательство: Springer Рейтинг: Цена: 23751.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained comprehensive volume covers a wide range of topics pertaining to new statistical methods in the health sciences, including epidemiology, pharmacovigilance, quality of life, survival analysis, and genomics.
The book will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics.
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