Download or read book Bulletin Institute of Mathematical Statistics written by Institute of Mathematical Statistics and published by . This book was released on 1994 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2007 with total page 1542 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistica Sinica written by and published by . This book was released on 1999 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Hands On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Download or read book Modern Statistics with R written by Måns Thulin and published by CRC Press. This book was released on 2024-08-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.
Download or read book AMSTAT News written by American Statistical Association and published by . This book was released on 2004 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Model Selection and Multimodel Inference written by Kenneth P. Burnham and published by Springer Science & Business Media. This book was released on 2007-05-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
Download or read book Data Analysis and Graphics Using R written by John Maindonald and published by Cambridge University Press. This book was released on 2006-12-26 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.
Download or read book Survival Analysis Using S written by Mara Tableman and published by CRC Press. This book was released on 2003-07-28 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regres
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Classification and Regression Trees written by Leo Breiman and published by Routledge. This book was released on 2017-10-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Download or read book Survival Analysis in Medicine and Genetics written by Jialiang Li and published by CRC Press. This book was released on 2013-06-04 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples. Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.
Download or read book Medical Data Analysis written by Rüdiger W. Brause and published by Springer. This book was released on 2003-07-31 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is a pleasure for us to present the contributions of the First International Symposium on Medical Data Analysis. Traditionally, the eld of medical data analysis can be devided into classical topics such as medical statistics, sur- val analysis, biometrics and medical informatics. Recently, however, time series analysis by physicists, machine learning and data mining with methods such as neural networks, Bayes networks or fuzzy computing by computer scientists have contributed important ideas to the led of medical data analysis. Although all these groups have similar intentions, there was nearly no exchange or discussion between them. With the growing possibilities for storing and ana- zing patient data, even in smaller health care institutions, the need for a rational treatment of all these data emerged as well. Therefore, the need for data exchange and presentation systems grew also. The goal of the symposium is to collect all these relevant aspects together. It provides an international forum for the sharing and exchange of original re- arch results, ideas and practical experiences among researchers and application developers from di erent areas related to medical applications dealing with the analysis of medical data. After a thorough reviewing process, 33 high quality papers were selected from the 45 international submissions. These contributions provided the di erent - pects of the eld in order to represent us with an exciting program.
Download or read book Digital Signal Processing with Kernel Methods written by Jose Luis Rojo-Alvarez and published by John Wiley & Sons. This book was released on 2017-12-22 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
Download or read book An Introduction to the Bootstrap written by Bradley Efron and published by CRC Press. This book was released on 1994-05-15 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Download or read book Bootstrap Methods and Their Application written by A. C. Davison and published by Cambridge University Press. This book was released on 1997-10-28 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk contains the library functions and documentation for use with Splus for Windows.
